Application of Machine Learning Algorithm and Artificial Intelligence in Improving Metabolic Syndrome related complications: A review
Application of Machine Learning Algorithm and Artificial Intelligence in Improving Metabolic Syndrome
Abstract
Aim: This review provides a concise summary of the utilisation of artificial intelligence (AI) in the context of metabolic diseases and their impact on overall well-being. The primary emphasis is placed on exploring the potential applications and addressing the issues associated with employing AI-based methodologies for both research purposes and clinical treatment in the context of non-communicable diseases. Methods: The relevant published publications were summarised by conducting computerised literature searches on several reputable databases using specific keywords such as MS, Artificial Intelligence (AI), Machine Learning (ML), Coronary Heart Disease, Obesity, and dyslipidemia. The researchers picked papers that had unique data and integrated the significant findings from these studies into the conclusion, which pertains to the present state of Metabolic Syndrome. Results: In summary, although the utilisation of artificial intelligence in educational interventions shows potential, it is important to acknowledge its inherent limits. Although there is a growing body of literature on the utilisation of digital and intelligent tools in the management of MS, a significant proportion of relevant studies suffer from limitations such as insufficient sample sizes or a failure to establish the clinical significance of the tested interventions. Notwithstanding these challenges, the advancements in utilising artificial intelligence (AI) in the field of medicine have been rapidly evolving, and it is imperative to acknowledge the potential and scholarly significance of these applications. Conclusion: The integration and comprehensive utilisation of certain artificial intelligence (AI) technologies can enable future health education on MS to provide comprehensive, personalised, and intelligent training. This intervention will provide patients with enduring protection and ongoing guidance throughout their lives.
Keywords:
Artificial Intelligence, Machine learning, Metabolic Syndrome
Downloads
Download data is not yet available.
References
Alaa, A. M., Bolton, T., Di Angelantonio, E., Rudd, J. H., & Van der Schaar, M. (2019). Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PloS one, 14(5). https://doi.org/10.1371/journal.pone.0213653
Alberti, K. G. M., Zimmet, P., & Shaw, J. (2005). The metabolic syndrome—a new worldwide definition. The Lancet, 366(9491), 1059-1062. https://doi.org/10.1016/S0140-6736(05)67402-8
Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., &Kwak, K. S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114, 23-43. https://doi.org/10.1016/j.future.2020.07.047
Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., &Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208-222. https://doi.org/10.1016/j.inffus.2020.06.008
Ankışhan, H. (2020). Blood pressure prediction from speech recordings. Biomedical Signal Processing and Control, 58, 101842. https://doi.org/10.1016/j.bspc.2019.101842
Aoun, A., Darwish, F., &Hamod, N. (2020). The influence of the gut microbiome on obesity in adults and the role of probiotics, prebiotics, and synbiotics for weight loss. Preventive nutrition and food science, 25(2), 113. https://doi.org/10.3746/pnf.2020.25.2.113
Argha, A., &Celler, B. G. (2019, July). Blood pressure estimation using time domain features of auscultatory waveforms and deep learning. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1821-1824). IEEE. https://doi.org/10.1109/EMBC.2019.8857464
Bailey, T. S., Chang, A., & Christiansen, M. (2014). Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm. Journal of diabetes science and technology, 9(2), 209-214. https://doi.org/10.1177/1932296814559746
Balkau, B. (1999). Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet. Med., 16, 442-443. https://doi.org/10.1046/j.1464-5491.1999.00059.x
Bang, S., Yoo, D., Kim, S. J., Jhang, S., Cho, S., & Kim, H. (2019). Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data. Scientific reports, 9(1), 10189. https://doi.org/10.1038/s41598-019-46249-x
Batbaatar, E., Park, K. H., Amarbayasgalan, T., Davagdorj, K., Munkhdalai, L., Pham, V. H., &Ryu, K. H. (2020). Class-incremental learning with deep generative feature replay for DNA methylation-based cancer classification. IEEE Access, 8, 210800-210815. https://doi.org/10.1109/ACCESS.2020.3039624
Baum, A., Scarpa, J., Bruzelius, E., Tamler, R., Basu, S., &Faghmous, J. (2017). Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. The lancet Diabetes & endocrinology, 5(10), 808-815. https://doi.org/10.1016/S2213-8587(17)30176-6
Beltrán-Sánchez, H., Harhay, M. O., Harhay, M. M., &McElligott, S. (2013). Prevalence and trends of metabolic syndrome in the adult US population, 1999–2010. Journal of the American College of Cardiology, 62(8), 697-703.https://doi.org/10.1016/j.jacc.2013.05.064
Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., Huttley, G. A., & Gregory Caporaso, J. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome, 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z
Bolyen, Evan &Rideout, Jai Ram & Dillon, Matthew &Bokulich, Nicholas &Abnet, Christian & Al-Ghalith, Gabriel & Alexander, Harriet &Alm, Eric &Arumugam, Manimozhiyan&Asnicar, Francesco &Bai, Yang &Bisanz, Jordan &Bittinger, Kyle &Brejnrod, Asker &Brislawn, Colin & Brown, C. Titus & Callahan, Benjamin &Caraballo Rodríguez, Andrés & Chase, John &Caporaso, J. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9
Caballero-Ruiz, E., García-Sáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2016). Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes. Expert Systems with Applications, 63, 386-396.https://doi.org/10.1016/j.eswa.2016.07.019
Caballero-Ruiz, E., García-Sáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2017). A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. International journal of medical informatics, 102, 35-49. https://doi.org/10.1016/j.ijmedinf.2017.02.014
Cao, S., &Zheng, H. (2022). A POI-based machine learning method for predicting residents’ health status. In Proceedings of the 2021 DigitalFUTURES: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) 3 (pp. 139-147). Springer Singapore.https://doi.org/10.1007/978-981-16-5983-6_13
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832 https://doi.org/10.3390/electronics8080832.
Catalogna, M., Cohen, E., Fishman, S., Halpern, Z., Nevo, U., & Ben-Jacob, E. (2012). Artificial neural networks based controller for glucose monitoring during clamp test. https://doi.org/10.1371/journal.pone.0044587
Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., & Yang, J. (2009, November). PFID: Pittsburgh fast-food image dataset. In 2009 16th IEEE International Conference on Image Processing (ICIP) (pp. 289-292). IEEE.https://doi.org/10.1109/ICIP.2009.5413511
Chen, S., Kuhn, M., Prettner, K., & Bloom, D. E. (2018). The macroeconomic burden of noncommunicable diseases in the United States: Estimates and projections. PloS one, 13(11), e0206702. https://doi.org/10.1109/ICIP.2009.5413511
Chen, Y., Subburathinam, A., Chen, C. H., &Zaki, M. J. (2021, March). Personalized food recommendation as constrained question answering over a large-scale food knowledge graph. In Proceedings of the 14th ACM international conference on web Search and data mining (pp. 544-552).https://doi.org/10.1145/3437963.3441816
Choudhury, S. R., Das, P., Koner, S., Ghosh, J., & Singh, K. (2024). Cardiovascular Disease Risks Related to Consumption of Ready-To-Eat Food Products between Young Adults of Kolkata, West Bengal, India. Journal of Comprehensive Health• Volume, 12(1), 60. https://dx.doi.org/10.25259/JCH_11_2023
Choudhury, S. R., Ghosh, J., Koner, S., Singh, K., &Bera, A. (2022). Traditional Indian Food for Improving Brain Cognition. Acta Scientific Neurology, 5(12). 43-58. https://doi.org/10.31080/ASNE.2022.05.0561
Consultation, W. H. O. (1999). Definition, diagnosis and classification of diabetes mellitus and its complications.https://www.staff.ncl.ac.uk/philip.home/who_dmc.htm
Contreras, I., &Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research, 20(5), e10775. https://doi.org/10.2196/10775
Dagliati, A., Malovini, A., Decata, P., Cogni, G., Teliti, M., Sacchi, L., Cerra, C., Chiovato, L., &Bellazzi, R. (2017). Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016, 470–479. https://pubmed.ncbi.nlm.nih.gov/28269842/
Dagliati, A., Sacchi, L., Tibollo, V., Cogni, G., Teliti, M., Martinez-Millana, A., Traver, V., Segagni, D., Posada, J., Ottaviano, M., Fico, G., Arredondo, M. T., De Cata, P., Chiovato, L., &Bellazzi, R. (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association : JAMIA, 25(5), 538–547 https://doi.org/10.1093/jamia/ocx159
Daskalaki, E., Diem, P., &Mougiakakou, S. G. (2016). Model-free machine learning in biomedicine: Feasibility study in type 1 diabetes. PloS one, 11(7), e0158722. https://doi.org/10.1371/journal.pone.0158722
Davagdorj, K., Bae, J. W., Pham, V. H., Theera-Umpon, N., &Ryu, K. H. (2021). Explainable artificial intelligence-based framework for non-communicable diseases prediction. Ieee Access, 9, 123672-123688. https://doi.org/10.1109/ACCESS.2021.3110336
Davagdorj, K., Lee, J. S., Park, K. H., &Ryu, K. H. (2021, April). Cost-sensitive neural network for prediction of hypertension using class imbalance dataset. In Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the 16th International Conference on IIHMSP in conjunction with the 13th international conference on FITAT, November 5-7, 2020, Ho Chi Minh City, Vietnam, Volume 2 (pp. 44-51). Singapore: Springer Singaporehttp://dx.doi.org/10.1007/978-981-33-6757-9_6
Davagdorj, K., Lee, J. S., Park, K. H., Van Huy, P., &Ryu, K. H. (2020). Synthetic oversampling based decision support framework to solve class imbalance problem in smoking cessation program. International Journal of Applied Science and Engineering, 17(3), 223-235. ISSN: 1727-7841 (Online)
Davagdorj, K., Lee, J. S., Pham, V. H., &Ryu, K. H. (2020). A comparative analysis of machine learning methods for class imbalance in a smoking cessation intervention. Applied Sciences, 10(9), 3307.https://doi.org/10.3390/app10093307
Davagdorj, K., Pham, V. H., Theera-Umpon, N., &Ryu, K. H. (2020). XGBoost-based framework for smoking-induced noncommunicable disease prediction. International journal of environmental research and public health, 17(18), 6513. https://doi.org/10.3390/ijerph17186513
Davagdorj, K., Yu, S. H., Kim, S. Y., Huy, P. V., Park, J. H., &Ryu, K. H. (2019). Prediction of 6 months smoking cessation program among women in Korea. Int. J. Mach. Learn. Comput, 9(1), 83-90. http://dx.doi.org/10.18178/ijmlc.2019.9.1.769
Davis, C. D. (2016). The gut microbiome and its role in obesity. Nutrition today, 51(4), 167-174. https://doi.org/10.1097/NT.0000000000000167
Dinani, S. T., Zekri, M., &Kamali, M. (2015). Regulation of blood glucose concentration in type 1 diabetics using single order sliding mode control combined with fuzzy on-line tunable gain, a simulation study. Journal of Medical Signals & Sensors, 5(3), 131-140..https://doi.org/10.4103/2228-7477.161463
Einhorn, D. (2003). American College of Endocrinology position statement on the insulin resistance syndrome. Endocrine practice, 9, 5-21. https://doi.org/10.1016/j.amjhyper.2004.08.008
El-Sappagh, S., Saleh, H., Sahal, R., Abuhmed, T., Islam, S. R., Ali, F., &Amer, E. (2021). Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data. Future Generation Computer Systems, 115, 680-699. https://doi.org/10.1016/j.future.2020.10.005
Elshawi, R., Al-Mallah, M. H., &Sakr, S. (2019). On the interpretability of machine learning-based model for predicting hypertension. BMC medical informatics and decision making, 19, 1-32. https://doi.org/10.1186/s12911-019-0874-0
Eskin, Y., & Mihailidis, A. (2012). An intelligent nutritional assessment system. In 2012 AAAI fall symposium series. https://www.semanticscholar.org/paper/An-Intelligent-Nutritional-Assessment-System-Eskin-Mihailidis/7f9a8a50efab7e9fc66b24450cf40a1fe6463539
Esmaelpoor, J., Moradi, M. H., &Kadkhodamohammadi, A. (2020). A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals. Computers in Biology and Medicine, 120, 103719. https://doi.org/10.1016/j.compbiomed.2020.103719
Esmaelpoor, J., Moradi, M. H., & Kadkhodamohammadi, A. (2020). A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals. Computers in Biology and Medicine, 120, 103719.https://doi.org/10.1016/j.compbiomed.2020.103719
Everett, E., Kane, B., Yoo, A., Dobs, A., & Mathioudakis, N. (2018). A novel approach for fully automated, personalized health coaching for adults with prediabetes: pilot clinical trial. Journal of medical Internet research, 20(2), e72. https://doi.org/10.2196/jmir.9723
Expert Panel on Detection, E. (2001). Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Jama, 285(19), 2486-2497.https://doi.org/10.1001/jama.285.19.2486
Fang, G., Xu, P., & Liu, W. (2020). Automated ischemic stroke subtyping based on machine learning approach. IEEE Access, 8, 118426-118432 https://doi.org/10.1109/ACCESS.2020.3004977.
Fereydouneyan, F., Zare, A., &Mehrshad, N. (2011). Using a fuzzy controller optimized by a genetic algorithm to regulate blood glucose level in type 1 diabetes. Journal of Medical Engineering & Technology, 35(5), 224-230.https://doi.org/10.3109/03091902.2011.569050
Fico, G., Arredondo, M. T., Protopappas, V., Georgia, E., & Fotiadis, D. (2015). Mining data when technology is applied to support patients and professional on the control of chronic diseases: the experience of the METABO platform for diabetes management. Data mining in clinical medicine, 191-216. https://doi.org/10.1007/978-1-4939-1985-7_13
Fico, G., Fioravanti, A., Arredondo, M. T., Ardigó, D., &Guillén, A. (2010, August). A healthy lifestyle coaching-persuasive application for patients with type 2 diabetes. In 2010 Annual international conference of the IEEE engineering in medicine and biology (pp. 2221-2224). IEEE..https://doi.org/10.1109/iembs.2010.5626185
Fico, G., Fioravanti, A., Arredondo, M. T., Gorman, J., Diazzi, C., Arcuri, G., Conti, C., &Pirini, G. (2016). Integration of Personalized Healthcare Pathways in an ICT Platform for Diabetes Managements: A Small-Scale Exploratory Study. IEEE journal of biomedical and health informatics, 20(1), 29–38 https://doi.org/10.1109/jbhi.2014.2367863.
Fico, G., Fioravanti, A., Arredondo, M. T., Leuteritz, J. P., Guillén, A., & Fernandez, D. (2011, August). A user centered design approach for patient interfaces to a diabetes IT platform. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1169-1172). IEEE.. https://doi.org/10.1109/iembs.2011.6090274
Fioravanti, A., Fico, G., Arredondo, M. T., &Leuteritz, J. P. (2011, August). A mobile feedback system for integrated E-health platforms to improve self-care and compliance of diabetes mellitus patients. In 2011 annual international conference of the IEEE engineering in medicine and biology society (pp. 3550-3553). IEEE. https://doi.org/10.1109/iembs.2011.6090591
Folkersen, L., Pain, O., Ingason, A., Werge, T., Lewis, C. M., & Austin, J. (2020). Impute. me: an open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Frontiers in genetics, 11, 515901.https://doi.org/10.3389/fgene.2020.00578
FordES, G. (2002). DietzWH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAmA, 287(3), 356-359. https://doi.org/10.1001/jama.287.3.356
Frøisland, D. H., &Årsand, E. (2015). Integrating visual dietary documentation in mobile-phone-based self-management application for adolescents with type 1 diabetes. Journal of diabetes science and technology, 9(3), 541-548.https://doi.org/10.1177/1932296815576956
Fu, Q., Sun, M., Tang, W., Wang, Z., Cao, M., Zhu, Z., Lu, L., Bi, Y., Ning, G., & Yang, T. (2014). A Chinese risk score model for identifying postprandial hyperglycemia without oral glucose tolerance test. Diabetes/metabolism research and reviews, 30(4), 284–290. https://doi.org/10.1002/dmrr.2490
GBD 2013 Risk Factors Collaborators, Forouzanfar, M. H., Alexander, L., Anderson, H. R., Bachman, V. F., Biryukov, S., Brauer, M., Burnett, R., Casey, D., Coates, M. M., Cohen, A., Delwiche, K., Estep, K., Frostad, J. J., Astha, K. C., Kyu, H. H., Moradi-Lakeh, M., Ng, M., Slepak, E. L., Thomas, B. A., Murray, C. J. (2015). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet (London, England), 386(10010), 2287–2323. https://doi.org/10.1016/s0140-6736(15)00128-2
Ge, R., Zhang, R., & Wang, P. (2020). Prediction of chronic diseases with multi-label neural network. IEEE Access, 8, 138210-138216. https://doi.org/10.1109/ACCESS.2020.3011374
Ghosh, J. (2023). A review on understanding the risk factors for coronary heart disease in Indian college students. International Journal of Noncommunicable Diseases, 8(3), 117-128. http://dx.doi.org/10.4103/jncd.jncd_68_23
Ghosh, J., CHAUDHURI, A. N., SAHA, I., & CHAUDHURI, D. (2021). Antimicrobial Effect of 1, 25 Dihydroxy Vitamin D on Vibrio cholerae and its Association with Serum 25-Hydroxy Vitamin D Level in Rural Elderly Women: An Experimental Study. Journal of Clinical & Diagnostic Research, (10),OC34-OC37.http://dx.doi.org/10.7860/JCDR/2021/49439.15571
Ghosh, J., Chaudhuri, A. N., Saha, I., &Chaudhuri, D. (2023). Antimicrobial Effect of 1, 25 Dihydroxy Vitamin D on Escherichia coli and its Association with Serum 25 Hydroxy Vitamin D Level: An Experimental Study on the Elderly Women. Acta Scientific MICROBIOLOGY, 6(4),27-34.http://actascientific.com/ASMI/pdf/ASMI-06-1228.pdf
Ghosh, J., Chaudhuri, D., Saha, I., &Chaudhuri, A. N. (2020). Prevalence of metabolic syndrome, vitamin D level, and their association among elderly women in a rural community of West Bengal, India. Medical Journal of Dr. DY Patil University, 13(4), 315-320. http://dx.doi.org/10.4103/mjdrdypu.mjdrdypu_229_19
Ghosh, J., Chaudhuri, D., Saha, I., &Chaudhuri, A. N. (2021). Period of sun exposure and vitamin d status among the rural elderly women of West Bengal, India. Indian Journal of Community Medicine, 46(2), 285-287. https://doi.org/10.4103/ijcm.ijcm_764_20
Ghosh, J., Chaudhuri,D., Saha, I., &Chaudhuri, A.N.(2022.Prevalence of Anemia and its Association with Metabolic Syndrome and its Components Among Rural Elderly Women of Amdanga block, North 24th PGS West Bengal. Indian Journal of Gerontology, 36(2),148-158. http://dx.doi.org/10.1016/S2352-4642(23)00316-4
Ghosh, J., Singh, K., Choudhury, S. R., Koner, S., Basu, N., &Maity, S. (2022). Impact of Diet and Nutrition on Memory T Cell Development, Maintenance and Function in the Context of Healthy Immune System. Acta Scientific NUTRITIONAL HEALTH, 6(8),142-154.https://actascientific.com/ASNH/pdf/ASNH-06-1108.pdf
Gomez, A. M., Alfonso-Cristancho, R., Orozco, J. J., Lynch, P. M., Prieto, D., Saunders, R., Roze, S., & Valencia, J. E. (2016). Clinical and economic benefits of integrated pump/CGM technology therapy in patients with type 1 diabetes in Colombia. Beneficiosclínicos y económicos de la terapia con bomba de insulinaintegrada a sistema de monitoreo continuo de glucosa en los pacientesdiabéticostipo 1 en Colombia. Endocrinologia y nutricion :organo de la Sociedad Espanola de Endocrinologia y Nutricion, 63(9), 466–474. https://doi.org/10.1016/j.endoen.2016.10.009
Grundy, S. M. (2008). Metabolic syndrome pandemic. Arteriosclerosis, thrombosis, and vascular biology, 28(4), 629-636.https://doi.org/10.1161/ATVBAHA.107.151092
Grundy, S. M., Hansen, B., Smith Jr, S. C., Cleeman, J. I., Kahn, R. A., & Conference Participants. (2004). Clinical management of metabolic syndrome: report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association conference on scientific issues related to management. Circulation, 109(4), 551-556. https://doi.org/10.1161/01.CIR.0000112379.88385.67
Guillén, A., Colás, J., Fico, G., &Guillén, S. (2011). METABO: A new paradigm towards diabetes disease management. An innovative business model. In 2011 Annual international conference of the IEEE engineering in medicine and biology society (pp. 3554-3557). IEEE. https://doi.org/10.1109/IEMBS.2011.6090592
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410. http://dx.doi.org/10.1001/jama.2016.17216
Hazlehurst, B. L., Lawrence, J. M., Donahoo, W. T., Sherwood, N. E., Kurtz, S. E., Xu, S., & Steiner, J. F. (2014). Automating assessment of lifestyle counseling in electronic health records. American journal of preventive medicine, 46(5), 457-464. http://dx.doi.org/10.1115/1.4058106
Herrero, P., Bondia, J., Adewuyi, O., Pesl, P., El-Sharkawy, M., Reddy, M., Toumazou, C., Oliver, N., & Georgiou, P. (2017). Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability. Computer methods and programs in biomedicine, 146, 125–131. https://doi.org/10.1016/j.cmpb.2017.05.010
Herrero, P., Pesl, P., Reddy, M., Oliver, N., Georgiou, P., &Toumazou, C. (2014). Advanced insulin bolus advisor based on run-to-run control and case-based reasoning. IEEE journal of biomedical and health informatics, 19(3), 1087-1096.https://doi.org/10.1109/JBHI.2014.2331896
Hidalgo, J. I., Maqueda, E., Risco-Martín, J. L., Cuesta-Infante, A., Colmenar, J. M., & Nobel, J. (2014). glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes. Journal of biomedical informatics, 48, 183-192. https://doi.org/10.1016/j.jbi.2013.12.015
Hu, Y., Gui, Z., Wang, J., & Li, M. (2022). Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation. International Journal of Geographical Information Science, 36(4), 799-821. https://doi.org/10.1080/13658816.2021.1968407
Ijaz, M. F., Attique, M., & Son, Y. (2020). Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors, 20(10), 2809. https://doi.org/10.3390/s20102809
Jacobs, P. G., Resalat, N., El Youssef, J., Reddy, R., Branigan, D., Preiser, N., ... & Castle, J. (2015). Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate. Journal of diabetes science and technology, 9(6), 1175-1184. https://doi.org/10.1177/1932296815609371
Jasner, Y., Belogolovski, A., Ben-Itzhak, M., Koren, O., &Louzoun, Y. (2021). Microbiome preprocessing machine learning pipeline. Frontiers in Immunology, 12, 677870. https://doi.org/10.3389/fimmu.2021.677870
Jiang, Fei& Jiang, Yong &Zhi, Hui& Dong, Yi & Li, Hao& Ma, Sufeng& Wang, Yilong& Dong, Qiang&Shen, Haipeng& Wang, Yongjun. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
Jiang, L., Qiu, B., Liu, X., Huang, C., & Lin, K. (2020). DeepFood: food image analysis and dietary assessment via deep model. IEEE Access, 8, 47477-47489. https://doi.org/10.1109/ACCESS.2020.2973625
Kaabouch, N., Hu, W. C., Chen, Y., Anderson, J. W., Ames, F., & Paulson, R. (2010). Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images. Journal of biomedical optics, 15(6), 061715-061715.https://doi.org/10.1117/1.3524233
KamelBoulos, M. N., Peng, G., &VoPham, T. (2019). An overview of GeoAI applications in health and healthcare. International journal of health geographics, 18(1), 7.https://doi.org/10.1186/s12942-019-0171-2
Kathirvel, S., & Thakur, J. S. (2018). Sustainable development goals and noncommunicable diseases: Roadmap till 2030–A plenary session of world noncommunicable diseases congress 2017. International Journal of Noncommunicable Diseases, 3(1), 3-8. http://dx.doi.org/10.1158/1538-7445.OVARIAN23-B045
Katigari, M. R., Ayatollahi, H., Malek, M., &Haghighi, M. K. (2017). Fuzzy expert system for diagnosing diabetic neuropathy. World journal of diabetes, 8(2), 80. https://doi.org/10.4239%2Fwjd.v8.i2.80
Kerasidou, A. (2020). Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bulletin of the World Health Organization, 98(4), 245. https://doi.org/10.2471%2FBLT.19.237198
Khooban, M. H., ABADI, D. N., Alfi, A., &Siahi, M. (2013). Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model. Turkish Journal of Electrical Engineering and Computer Sciences, 21(7), 2110-2126. http://dx.doi.org/10.1109/IranianCEE.2012.6292429
Kopylova, E., Noé, L., &Touzet, H. (2012). SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28(24), 3211-3217. https://doi.org/10.1093/bioinformatics/bts611
Krittanawong, C. (2018). The rise of artificial intelligence and the uncertain future for physicians. European journal of internal medicine, 48, e13-e14. https://doi.org/10.1016/j.ejim.2017.06.017
Kumari, V. A., &Chitra, R. (2013). Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications, 3(2), 1797-1801. http://dx.doi.org/10.1158/1538-7445.OVARIAN23-B058
Lambert, S. A., Gil, L., Jupp, S., Ritchie, S. C., Xu, Y., Buniello, A., ... & Inouye, M. (2021). The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics, 53(4), 420-425. http://dx.doi.org/10.1038/s41588-021-00783-5
LaPierre, N., Ju, C. J. T., Zhou, G., & Wang, W. (2019). MetaPheno: a critical evaluation of deep learning and machine learning in metagenome-based disease prediction. Methods, 166, 74-82. https://doi.org/10.1016/j.ymeth.2019.03.003
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., &Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature communications, 11(1), 3852. http://dx.doi.org/10.2337/db21-47-OR
Li, J., Huang, J., Zheng, L., & Li, X. (2020). Application of artificial intelligence in diabetes education and management: present status and promising prospect. Frontiers in public health, 8, 521222. https://doi.org/10.3389/fpubh.2020.00173
Liang, Q., Bible, P. W., Liu, Y., Zou, B., & Wei, L. (2020). DeepMicrobes: taxonomic classification for metagenomics with deep learning. NAR Genomics and Bioinformatics, 2(1), lqaa009. https://doi.org/10.1093/nargab/lqaa009 Published: 19 February 2020 Article history
Lotfata, A. (2022). Using geographically weighted models to explore obesity prevalence association with air temperature, socioeconomic factors, and unhealthy behavior in the USA. Journal of Geovisualization and Spatial Analysis, 6(1), 14. http://dx.doi.org/10.1007/s41651-022-00108-y
Luo, H., Yang, D., Barszczyk, A., Vempala, N., Wei, J., Wu, S. J., Zheng, P. P., Fu, G., Lee, K., &Feng, Z. P. (2019). Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. Circulation. Cardiovascular imaging, 12(8), e008857. https://doi.org/10.1161/CIRCIMAGING.119.008857
Maharana, A., &Nsoesie, E. O. (2018). Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. JAMA network open, 1(4), e181535-e181535..https://doi.org/10.1001/jamanetworkopen.2018.1535
Mahé, F., Rognes, T., Quince, C., de Vargas, C., &Dunthorn, M. (2014). Swarm: robust and fast clustering method for amplicon-based studies. PeerJ, 2, e593.https://doi.org/10.7717/peerj.593
Mani, S., Chen, Y., Elasy, T., Clayton, W., & Denny, J. (2012). Type 2 diabetes risk forecasting from EMR data using machine learning. In AMIA annual symposium proceedings (Vol. 2012, p. 606). American Medical Informatics Association. http://dx.doi.org/10.1158/1538-7445.AM2024-1831
Mauseth, R., Hirsch, I. B., Bollyky, J., Kircher, R., Matheson, D., Sanda, S., &Greenbaum, C. (2013). Use of a “fuzzy logic” controller in a closed-loop artificial pancreas. Diabetes technology & therapeutics, 15(8), 628-633. https://doi.org/10.1089/dia.2013.0036
Mauseth, R., Lord, S. M., Hirsch, I. B., Kircher, R. C., Matheson, D. P., &Greenbaum, C. J. (2015). Stress testing of an artificial pancreas system with pizza and exercise leads to improvements in the system’s fuzzy logic controller. Journal of diabetes science and technology, 9(6), 1253-1259. https://doi.org/10.1177/1932296815602098
Mauseth, R., Wang, Y., Dassau, E., Kircher, R., Jr, Matheson, D., Zisser, H., Jovanovic, L., & Doyle, F. J., 3rd (2010). Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. Journal of diabetes science and technology, 4(4), 913–922. https://doi.org/10.1177/193229681000400422
Miao, F., Wen, B., Hu, Z., Fortino, G., Wang, X. P., Liu, Z. D., Tang, M., & Li, Y. (2020). Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques. Artificial intelligence in medicine, 108, 101919. https://doi.org/10.1016/j.artmed.2020.101919
Mieth, B., Rozier, A., Rodriguez, J. A., Höhne, M. M., Görnitz, N., & Müller, K. R. (2021). DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies. NAR genomics and bioinformatics, 3(3), lqab065. https://doi.org/10.1093/nargab/lqab065
Miller, J. M., Kaylor, M. B., Johannsson, M., Bay, C., &Churilla, J. R. (2014). Prevalence of metabolic syndrome and individual criterion in US adolescents: 2001–2010 National Health and Nutrition Examination Survey. Metabolic syndrome and related disorders, 12(10), 527-532. https://doi.org/10.1089/met.2014.0055
Miller, S., Nimri, R., Atlas, E., Grunberg, E. A., & Phillip, M. (2011). Automatic learning algorithm for the MD-logic artificial pancreas system. Diabetes technology & therapeutics, 13(10), 983-990. https://doi.org/10.1089/dia.2010.0216
Moghissi, E. S., Korytkowski, M. T., DiNardo, M., Einhorn, D., Hellman, R., Hirsch, I. B., Inzucchi, S. E., Ismail-Beigi, F., Kirkman, M. S., Umpierrez, G. E., American Association of Clinical Endocrinologists, & American Diabetes Association (2009). American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes care, 32(6), 1119–1131. https://doi.org/10.2337/dc09-9029.
Mohan, S., Thirumalai, C., &Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 7, 81542-81554. https://doi.org/10.1109/ACCESS.2019.2923707
Monte-Moreno, E. (2011). Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artificial intelligence in medicine, 53(2), 127-138. https://doi.org/10.1016/j.artmed.2011.05.001
Mottillo, S., Filion, K. B., Genest, J., Joseph, L., Pilote, L., Poirier, P., ... & Eisenberg, M. J. (2010). The metabolic syndrome and cardiovascular risk: a systematic review and meta-analysis. Journal of the American College of Cardiology, 56(14), 1113-1132.https://doi.org/10.1016/j.jacc.2010.05.034
Noble, D., Mathur, R., Dent, T., Meads, C., &Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. Bmj, 343. https://doi.org/10.1136/bmj.d7163
Oh, M., & Zhang, L. (2020). DeepMicro: deep representation learning for disease prediction based on microbiome data. Scientific reports, 10(1), 6026. https://doi.org/10.1038/s41598-020-63159-5
Oka, R., Nomura, A., Yasugi, A., Kometani, M., Gondoh, Y., Yoshimura, K., &Yoneda, T. (2019). Study protocol for the effects of artificial intelligence (AI)-supported automated nutritional intervention on glycemic control in patients with type 2 diabetes mellitus. Diabetes Therapy, 10, 1151-1161. https://doi.org/10.1007/s13300-019-0595-5
Panigrahi, R., Borah, S., Bhoi, A. K., Ijaz, M. F., Pramanik, M., Jhaveri, R. H., &Chowdhary, C. L. (2021). Performance assessment of supervised classifiers for designing intrusion detection systems: a comprehensive review and recommendations for future research. Mathematics, 9(6), 690. https://doi.org/10.3390/math9060690
Panigrahi, R., Borah, S., Bhoi, A. K., Ijaz, M. F., Pramanik, M., Kumar, Y., &Jhaveri, R. H. (2021). A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics, 9(7), 751. https://doi.org/10.3390/math9070751
Pańkowska, E., Błazik, M., &Groele, L. (2012). Does the fat-protein meal increase postprandial glucose level in type 1 diabetes patients on insulin pump: the conclusion of a randomized study. Diabetes technology & therapeutics, 14(1), 16-22. https://doi.org/10.1089/dia.2011.0083
Park, D., Kim, K., Kim, S., Spranger, M., & Kang, J. (2021). FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings. Scientific reports, 11(1), 931. https://doi.org/10.1038/s41598-020-79422-8
Park, K. H., Pham, V. H., Davagdorj, K., Munkhdalai, L., &Ryu, K. H. (2021, April). A subtype classification of hematopoietic cancer using machine learning approach. In Asian Conference on Intelligent Information and Database Systems (pp. 113-121). Singapore: Springer Singapore. http://dx.doi.org/10.1007/978-981-16-1685-3_10
Pesl, P .,Herrero, P., Reddy, M., Xenou, Maria., Oliver, Nick., Johnston, Desmond ., Toumazou, Christofer., Georgiou, P. (2015). An advanced bolus calculator for type 1 diabetes: system architecture and usability results. IEEE journal of biomedical and health informatics, 20(1), 11-17. https://doi.org/10.1109/JBHI.2015.2464088
Pesl, P., Herrero, P., Reddy, M., Oliver, N., Johnston, D. G., Toumazou, C., & Georgiou, P. (2017). Case-based reasoning for insulin bolus advice: evaluation of case parameters in a six-week pilot study. Journal of diabetes science and technology, 11(1), 37-42. https://doi.org/10.1177/1932296816629986
Picone, D. S., Deshpande, R. A., Schultz, M. G., Fonseca, R., Campbell, N. R. C., Delles, C., Hecht Olsen, M., Schutte, A. E., Stergiou, G., Padwal, R., Zhang, X. H., & Sharman, J. E. (2020). Nonvalidated Home Blood Pressure Devices Dominate the Online Marketplace in Australia: Major Implications for Cardiovascular Risk Management. Hypertension (Dallas, Tex.: 1979), 75(6), 1593–1599. https://doi.org/10.1161/HYPERTENSIONAHA.120.14719
Pinto, Mauro & Oliveira, Hugo & Batista, Sonia & Cruz, Luis & Pinto, Mafalda&Correia, Inês& Martins, Pedro & Teixeira, César. (2020). Prediction of disease progression and outcomes in multiple sclerosis with machine learning. Scientific reports, 10(1), 21038. https://doi.org/10.1038/s41598-020-78212-6
Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., &Areán, P. A. (2019). The accuracy of passive phone sensors in predicting daily mood. Depression and anxiety, 36(1), 72-81. https://doi.org/10.1002/da.22822
Quinlan, J. R. (1981). Discovering rules by induction from collections of examples. Expert System in the Micro-Electronic Age, 168-201. https://doi.org/10.1016/0020-7373(92)90055-P
Quinlan, J. R. (1993). Program for machine learning. C4. 5. https://doi.org/10.1007/BF00993309
Reddy, M., Pesl, P., Xenou, M., Toumazou, C., Johnston, D., Georgiou, P., Herrero, P., & Oliver, N. (2016). Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study. Diabetes technology & therapeutics, 18(8), 487–493. http://dx.doi.org/10.1089/dia.2015.0413
Rigla, M., Martínez-Sarriegui, I., García-Sáez, G., Pons, B., & Hernando, M. E. (2018). Gestational diabetes management using smart mobile telemedicine. Journal of diabetes science and technology, 12(2), 260-264. https://doi.org/10.1177/1932296817704442
Rivellese, A. A., Ventura, M. M., Vespasiani, G., Bruni, M., Moriconi, V., Pacioni, D., Sulpini, M. T., Borrelli, R., Giacco, A., &Cangemi, C. (1991). Evaluation of new computerized method for recording 7-day food intake in IDDM patients. Diabetes care, 14(7), 602–604. https://doi.org/10.2337/diacare.14.7.602
Rochlani, Y., Pothineni, N. V., Kovelamudi, S., & Mehta, J. L. (2017). Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Therapeutic advances in cardiovascular disease, 11(8), 215-225. https://doi.org/10.1177/1753944717711379
Russell, S. J., &Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson. https://thuvienso.hoasen.edu.vn/handle/123456789/8967
Sacchi, L., Dagliati, A., Segagni, D., Leporati, P., Chiovato, L., &Bellazzi, R. (2015, August). Improving risk-stratification of Diabetes complications using temporal data mining. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2131-2134). IEEE. https://doi.org/10.1109/EMBC.2015.7318810
Sharma, D., Paterson, A. D., &Xu, W. (2020). TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction. Bioinformatics, 36(17), 4544-4550. https://doi.org/10.1093/bioinformatics/btaa542
Sharma, V., Sharma, V., Khan, A., Wassmer, D. J., Schoenholtz, M. D., Hontecillas, R., Bassaganya-Riera, J., Zand, R., &Abedi, V. (2020). Malnutrition, Health and the Role of Machine Learning in Clinical Setting. Frontiers in nutrition, 7, 44. https://doi.org/10.3389/fnut.2020.00044
Singh, K., Choudhury, S. R., Ghosh, J., Koner, S., & Pal, R. (2023). Ageing and its relationship with psychoneuroimmunology. Journal of Comprehensive Health, 11(2). http://dx.doi.org/10.53553/JCH.v11i02.004
Srinivasan, A., Lee, J. B., Dassau, E., & Doyle III, F. J. (2014). Novel insulin delivery profiles for mixed meals for sensor-augmented pump and closed-loop artificial pancreas therapy for type 1 diabetes mellitus. Journal of diabetes science and technology, 8(5), 957-968. https://doi.org/10.1177/1932296814543660
Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21(8), 2852. https://doi.org/10.3390/s21082852
Steinfeldt, J., Buergel, T., Loock, L., Kittner, P., Ruyoga, G., ZuBelzen, J. U., Sasse, S., Strangalies, H., Christmann, L., Hollmann, N., Wolf, B., Ference, B., Deanfield, J., Landmesser, U., &Eils, R. (2022). Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort. The Lancet. Digital health, 4(2), e84–e94. https://doi.org/10.1016/s2589-7500(21)00249-1
Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PloS one, 12(6), e0179790. https://doi.org/10.1371/journal.pone.0179790
Uhlig, K., Patel, K., Ip, S., Kitsios, G. D., & Balk, E. M. (2013). Self-measured blood pressure monitoring in the management of hypertension: a systematic review and meta-analysis. Annals of internal medicine, 159(3), 185-194. https://doi.org/10.7326/0003-4819-159-3-201308060-00008
Usher, D., Dumskyj, M., Himaga, M., Williamson, T. H., Nussey, S., & Boyce, J. (2004). Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabetic Medicine, 21(1), 84-90. https://doi.org/10.1046/j.1464-5491.2003.01085.x
Walsh, J., Roberts, R., Weber, D., Faber-Heinemann, G., & Heinemann, L. (2015). Insulin pump and CGM usage in the United States and Germany: results of a real-world survey with 985 subjects. Journal of diabetes science and technology, 9(5), 1103-1110. https://doi.org/10.1177/1932296815588945
Wang, W., Duan, L. Y., Jiang, H., Jing, P., Song, X., &Nie, L. (2021). Market2Dish: health-aware food recommendation. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1), 1-19. https://doi.org/10.1145/3418211
Wang, Y., Bhattacharya, T., Jiang, Y., Qin, X., Wang, Y., Liu, Y., Saykin, A. J., & Chen, L. (2021). A novel deep learning method for predictive modeling of microbiome data. Briefings in bioinformatics, 22(3), bbaa073. http://dx.doi.org/10.1093/bib/bbaa073
WHO, G. (2017). Noncommunicable diseases progress monitor. https://www.who.int/publications/i/item/9789241513029
Wiens, J., &Shenoy, E. S. (2018). Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical infectious diseases, 66(1), 149-153. https://doi.org/10.1093/cid/cix731
World Health Organization. (2012). Action plan for implementation of the European strategy for the prevention and control of noncommunicable diseases 2012− 2016. https://iris.who.int/handle/10665/352659
World Health Organization. (2018). Noncommunicable diseases country profiles 2018. https://www.who.int/publications/i/item/9789241514620
World Health Organization. Nutrition topics:Nutrition for Older persons. WHO, 2021. https://www.who.int/nutrition/topics/ageing/en/.
Xiao, J., Ding, R., Xu, X., Guan, H., Feng, X., Sun, T., Zhu, S., & Ye, Z. (2019). Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. Journal of translational medicine, 17(1), 119. https://doi.org/10.1186/s12967-019-1860-0
Xu, X., Solon-Biet, S. M., Senior, A., Raubenheimer, D., Simpson, S. J., Fontana, L., Mueller, S., & Yang, J. Y. H. (2020). LC-N2G: a local consistency approach for nutrigenomics data analysis. BMC bioinformatics, 21(1), 530. https://doi.org/10.1186/s12859-020-03861-3
Yadav, J., Rani, A., & Singh, V. (2016). Performance analysis of fuzzy-PID controller for blood glucose regulation in type-1 diabetic patients. Journal of medical systems, 40(12), 254. https://doi.org/10.1007/s10916-016-0602-6
Yap, M. H., Chatwin, K. E., Ng, C. C., Abbott, C. A., Bowling, F. L., Rajbhandari, S., Boulton, A. J. M., & Reeves, N. D. (2018). A New Mobile Application for Standardizing Diabetic Foot Images. Journal of diabetes science and technology, 12(1), 169–173. https://doi.org/10.1177/1932296817713761
Yu, W., Liu, T., Valdez, R., Gwinn, M., &Khoury, M. J. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC medical informatics and decision making, 10, 1-7.https://doi.org/10.1186/1472-6947-10-16
Yun, T., Li, H., Chang, P. C., Lin, M. F., Carroll, A., & McLean, C. Y. (2020). Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics, 36(24), 5582-5589. https://doi.org/10.1093/bioinformatics/btaa1081
Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalová L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001
Zhang, S., Yao, L., Sun, A., &Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38. http://dx.doi.org/10.1145/3285029
Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S., &Delp, E. J. (2010). The use of mobile devices in aiding dietary assessment and evaluation. IEEE journal of selected topics in signal processing, 4(4), 756-766. https://doi.org/10.1109/JSTSP.2010.2051471
Zhu, Q., Jiang, X., & Zhu, Q. (2019). Graph embedding deep learning guides microbial biomarkers' identification. Frontiers in genetics, 10, 491009. https://doi.org/10.3389/fgene.2019.01182
Alberti, K. G. M., Zimmet, P., & Shaw, J. (2005). The metabolic syndrome—a new worldwide definition. The Lancet, 366(9491), 1059-1062. https://doi.org/10.1016/S0140-6736(05)67402-8
Ali, F., El-Sappagh, S., Islam, S. R., Ali, A., Attique, M., Imran, M., &Kwak, K. S. (2021). An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Generation Computer Systems, 114, 23-43. https://doi.org/10.1016/j.future.2020.07.047
Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., &Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208-222. https://doi.org/10.1016/j.inffus.2020.06.008
Ankışhan, H. (2020). Blood pressure prediction from speech recordings. Biomedical Signal Processing and Control, 58, 101842. https://doi.org/10.1016/j.bspc.2019.101842
Aoun, A., Darwish, F., &Hamod, N. (2020). The influence of the gut microbiome on obesity in adults and the role of probiotics, prebiotics, and synbiotics for weight loss. Preventive nutrition and food science, 25(2), 113. https://doi.org/10.3746/pnf.2020.25.2.113
Argha, A., &Celler, B. G. (2019, July). Blood pressure estimation using time domain features of auscultatory waveforms and deep learning. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1821-1824). IEEE. https://doi.org/10.1109/EMBC.2019.8857464
Bailey, T. S., Chang, A., & Christiansen, M. (2014). Clinical accuracy of a continuous glucose monitoring system with an advanced algorithm. Journal of diabetes science and technology, 9(2), 209-214. https://doi.org/10.1177/1932296814559746
Balkau, B. (1999). Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet. Med., 16, 442-443. https://doi.org/10.1046/j.1464-5491.1999.00059.x
Bang, S., Yoo, D., Kim, S. J., Jhang, S., Cho, S., & Kim, H. (2019). Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data. Scientific reports, 9(1), 10189. https://doi.org/10.1038/s41598-019-46249-x
Batbaatar, E., Park, K. H., Amarbayasgalan, T., Davagdorj, K., Munkhdalai, L., Pham, V. H., &Ryu, K. H. (2020). Class-incremental learning with deep generative feature replay for DNA methylation-based cancer classification. IEEE Access, 8, 210800-210815. https://doi.org/10.1109/ACCESS.2020.3039624
Baum, A., Scarpa, J., Bruzelius, E., Tamler, R., Basu, S., &Faghmous, J. (2017). Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. The lancet Diabetes & endocrinology, 5(10), 808-815. https://doi.org/10.1016/S2213-8587(17)30176-6
Beltrán-Sánchez, H., Harhay, M. O., Harhay, M. M., &McElligott, S. (2013). Prevalence and trends of metabolic syndrome in the adult US population, 1999–2010. Journal of the American College of Cardiology, 62(8), 697-703.https://doi.org/10.1016/j.jacc.2013.05.064
Bokulich, N. A., Kaehler, B. D., Rideout, J. R., Dillon, M., Bolyen, E., Knight, R., Huttley, G. A., & Gregory Caporaso, J. (2018). Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2's q2-feature-classifier plugin. Microbiome, 6(1), 90. https://doi.org/10.1186/s40168-018-0470-z
Bolyen, Evan &Rideout, Jai Ram & Dillon, Matthew &Bokulich, Nicholas &Abnet, Christian & Al-Ghalith, Gabriel & Alexander, Harriet &Alm, Eric &Arumugam, Manimozhiyan&Asnicar, Francesco &Bai, Yang &Bisanz, Jordan &Bittinger, Kyle &Brejnrod, Asker &Brislawn, Colin & Brown, C. Titus & Callahan, Benjamin &Caraballo Rodríguez, Andrés & Chase, John &Caporaso, J. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology, 37(8), 852–857. https://doi.org/10.1038/s41587-019-0209-9
Caballero-Ruiz, E., García-Sáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2016). Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes. Expert Systems with Applications, 63, 386-396.https://doi.org/10.1016/j.eswa.2016.07.019
Caballero-Ruiz, E., García-Sáez, G., Rigla, M., Villaplana, M., Pons, B., & Hernando, M. E. (2017). A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. International journal of medical informatics, 102, 35-49. https://doi.org/10.1016/j.ijmedinf.2017.02.014
Cao, S., &Zheng, H. (2022). A POI-based machine learning method for predicting residents’ health status. In Proceedings of the 2021 DigitalFUTURES: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) 3 (pp. 139-147). Springer Singapore.https://doi.org/10.1007/978-981-16-5983-6_13
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832 https://doi.org/10.3390/electronics8080832.
Catalogna, M., Cohen, E., Fishman, S., Halpern, Z., Nevo, U., & Ben-Jacob, E. (2012). Artificial neural networks based controller for glucose monitoring during clamp test. https://doi.org/10.1371/journal.pone.0044587
Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., & Yang, J. (2009, November). PFID: Pittsburgh fast-food image dataset. In 2009 16th IEEE International Conference on Image Processing (ICIP) (pp. 289-292). IEEE.https://doi.org/10.1109/ICIP.2009.5413511
Chen, S., Kuhn, M., Prettner, K., & Bloom, D. E. (2018). The macroeconomic burden of noncommunicable diseases in the United States: Estimates and projections. PloS one, 13(11), e0206702. https://doi.org/10.1109/ICIP.2009.5413511
Chen, Y., Subburathinam, A., Chen, C. H., &Zaki, M. J. (2021, March). Personalized food recommendation as constrained question answering over a large-scale food knowledge graph. In Proceedings of the 14th ACM international conference on web Search and data mining (pp. 544-552).https://doi.org/10.1145/3437963.3441816
Choudhury, S. R., Das, P., Koner, S., Ghosh, J., & Singh, K. (2024). Cardiovascular Disease Risks Related to Consumption of Ready-To-Eat Food Products between Young Adults of Kolkata, West Bengal, India. Journal of Comprehensive Health• Volume, 12(1), 60. https://dx.doi.org/10.25259/JCH_11_2023
Choudhury, S. R., Ghosh, J., Koner, S., Singh, K., &Bera, A. (2022). Traditional Indian Food for Improving Brain Cognition. Acta Scientific Neurology, 5(12). 43-58. https://doi.org/10.31080/ASNE.2022.05.0561
Consultation, W. H. O. (1999). Definition, diagnosis and classification of diabetes mellitus and its complications.https://www.staff.ncl.ac.uk/philip.home/who_dmc.htm
Contreras, I., &Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research, 20(5), e10775. https://doi.org/10.2196/10775
Dagliati, A., Malovini, A., Decata, P., Cogni, G., Teliti, M., Sacchi, L., Cerra, C., Chiovato, L., &Bellazzi, R. (2017). Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016, 470–479. https://pubmed.ncbi.nlm.nih.gov/28269842/
Dagliati, A., Sacchi, L., Tibollo, V., Cogni, G., Teliti, M., Martinez-Millana, A., Traver, V., Segagni, D., Posada, J., Ottaviano, M., Fico, G., Arredondo, M. T., De Cata, P., Chiovato, L., &Bellazzi, R. (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association : JAMIA, 25(5), 538–547 https://doi.org/10.1093/jamia/ocx159
Daskalaki, E., Diem, P., &Mougiakakou, S. G. (2016). Model-free machine learning in biomedicine: Feasibility study in type 1 diabetes. PloS one, 11(7), e0158722. https://doi.org/10.1371/journal.pone.0158722
Davagdorj, K., Bae, J. W., Pham, V. H., Theera-Umpon, N., &Ryu, K. H. (2021). Explainable artificial intelligence-based framework for non-communicable diseases prediction. Ieee Access, 9, 123672-123688. https://doi.org/10.1109/ACCESS.2021.3110336
Davagdorj, K., Lee, J. S., Park, K. H., &Ryu, K. H. (2021, April). Cost-sensitive neural network for prediction of hypertension using class imbalance dataset. In Advances in Intelligent Information Hiding and Multimedia Signal Processing: Proceeding of the 16th International Conference on IIHMSP in conjunction with the 13th international conference on FITAT, November 5-7, 2020, Ho Chi Minh City, Vietnam, Volume 2 (pp. 44-51). Singapore: Springer Singaporehttp://dx.doi.org/10.1007/978-981-33-6757-9_6
Davagdorj, K., Lee, J. S., Park, K. H., Van Huy, P., &Ryu, K. H. (2020). Synthetic oversampling based decision support framework to solve class imbalance problem in smoking cessation program. International Journal of Applied Science and Engineering, 17(3), 223-235. ISSN: 1727-7841 (Online)
Davagdorj, K., Lee, J. S., Pham, V. H., &Ryu, K. H. (2020). A comparative analysis of machine learning methods for class imbalance in a smoking cessation intervention. Applied Sciences, 10(9), 3307.https://doi.org/10.3390/app10093307
Davagdorj, K., Pham, V. H., Theera-Umpon, N., &Ryu, K. H. (2020). XGBoost-based framework for smoking-induced noncommunicable disease prediction. International journal of environmental research and public health, 17(18), 6513. https://doi.org/10.3390/ijerph17186513
Davagdorj, K., Yu, S. H., Kim, S. Y., Huy, P. V., Park, J. H., &Ryu, K. H. (2019). Prediction of 6 months smoking cessation program among women in Korea. Int. J. Mach. Learn. Comput, 9(1), 83-90. http://dx.doi.org/10.18178/ijmlc.2019.9.1.769
Davis, C. D. (2016). The gut microbiome and its role in obesity. Nutrition today, 51(4), 167-174. https://doi.org/10.1097/NT.0000000000000167
Dinani, S. T., Zekri, M., &Kamali, M. (2015). Regulation of blood glucose concentration in type 1 diabetics using single order sliding mode control combined with fuzzy on-line tunable gain, a simulation study. Journal of Medical Signals & Sensors, 5(3), 131-140..https://doi.org/10.4103/2228-7477.161463
Einhorn, D. (2003). American College of Endocrinology position statement on the insulin resistance syndrome. Endocrine practice, 9, 5-21. https://doi.org/10.1016/j.amjhyper.2004.08.008
El-Sappagh, S., Saleh, H., Sahal, R., Abuhmed, T., Islam, S. R., Ali, F., &Amer, E. (2021). Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data. Future Generation Computer Systems, 115, 680-699. https://doi.org/10.1016/j.future.2020.10.005
Elshawi, R., Al-Mallah, M. H., &Sakr, S. (2019). On the interpretability of machine learning-based model for predicting hypertension. BMC medical informatics and decision making, 19, 1-32. https://doi.org/10.1186/s12911-019-0874-0
Eskin, Y., & Mihailidis, A. (2012). An intelligent nutritional assessment system. In 2012 AAAI fall symposium series. https://www.semanticscholar.org/paper/An-Intelligent-Nutritional-Assessment-System-Eskin-Mihailidis/7f9a8a50efab7e9fc66b24450cf40a1fe6463539
Esmaelpoor, J., Moradi, M. H., &Kadkhodamohammadi, A. (2020). A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals. Computers in Biology and Medicine, 120, 103719. https://doi.org/10.1016/j.compbiomed.2020.103719
Esmaelpoor, J., Moradi, M. H., & Kadkhodamohammadi, A. (2020). A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals. Computers in Biology and Medicine, 120, 103719.https://doi.org/10.1016/j.compbiomed.2020.103719
Everett, E., Kane, B., Yoo, A., Dobs, A., & Mathioudakis, N. (2018). A novel approach for fully automated, personalized health coaching for adults with prediabetes: pilot clinical trial. Journal of medical Internet research, 20(2), e72. https://doi.org/10.2196/jmir.9723
Expert Panel on Detection, E. (2001). Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Jama, 285(19), 2486-2497.https://doi.org/10.1001/jama.285.19.2486
Fang, G., Xu, P., & Liu, W. (2020). Automated ischemic stroke subtyping based on machine learning approach. IEEE Access, 8, 118426-118432 https://doi.org/10.1109/ACCESS.2020.3004977.
Fereydouneyan, F., Zare, A., &Mehrshad, N. (2011). Using a fuzzy controller optimized by a genetic algorithm to regulate blood glucose level in type 1 diabetes. Journal of Medical Engineering & Technology, 35(5), 224-230.https://doi.org/10.3109/03091902.2011.569050
Fico, G., Arredondo, M. T., Protopappas, V., Georgia, E., & Fotiadis, D. (2015). Mining data when technology is applied to support patients and professional on the control of chronic diseases: the experience of the METABO platform for diabetes management. Data mining in clinical medicine, 191-216. https://doi.org/10.1007/978-1-4939-1985-7_13
Fico, G., Fioravanti, A., Arredondo, M. T., Ardigó, D., &Guillén, A. (2010, August). A healthy lifestyle coaching-persuasive application for patients with type 2 diabetes. In 2010 Annual international conference of the IEEE engineering in medicine and biology (pp. 2221-2224). IEEE..https://doi.org/10.1109/iembs.2010.5626185
Fico, G., Fioravanti, A., Arredondo, M. T., Gorman, J., Diazzi, C., Arcuri, G., Conti, C., &Pirini, G. (2016). Integration of Personalized Healthcare Pathways in an ICT Platform for Diabetes Managements: A Small-Scale Exploratory Study. IEEE journal of biomedical and health informatics, 20(1), 29–38 https://doi.org/10.1109/jbhi.2014.2367863.
Fico, G., Fioravanti, A., Arredondo, M. T., Leuteritz, J. P., Guillén, A., & Fernandez, D. (2011, August). A user centered design approach for patient interfaces to a diabetes IT platform. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1169-1172). IEEE.. https://doi.org/10.1109/iembs.2011.6090274
Fioravanti, A., Fico, G., Arredondo, M. T., &Leuteritz, J. P. (2011, August). A mobile feedback system for integrated E-health platforms to improve self-care and compliance of diabetes mellitus patients. In 2011 annual international conference of the IEEE engineering in medicine and biology society (pp. 3550-3553). IEEE. https://doi.org/10.1109/iembs.2011.6090591
Folkersen, L., Pain, O., Ingason, A., Werge, T., Lewis, C. M., & Austin, J. (2020). Impute. me: an open-source, non-profit tool for using data from direct-to-consumer genetic testing to calculate and interpret polygenic risk scores. Frontiers in genetics, 11, 515901.https://doi.org/10.3389/fgene.2020.00578
FordES, G. (2002). DietzWH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAmA, 287(3), 356-359. https://doi.org/10.1001/jama.287.3.356
Frøisland, D. H., &Årsand, E. (2015). Integrating visual dietary documentation in mobile-phone-based self-management application for adolescents with type 1 diabetes. Journal of diabetes science and technology, 9(3), 541-548.https://doi.org/10.1177/1932296815576956
Fu, Q., Sun, M., Tang, W., Wang, Z., Cao, M., Zhu, Z., Lu, L., Bi, Y., Ning, G., & Yang, T. (2014). A Chinese risk score model for identifying postprandial hyperglycemia without oral glucose tolerance test. Diabetes/metabolism research and reviews, 30(4), 284–290. https://doi.org/10.1002/dmrr.2490
GBD 2013 Risk Factors Collaborators, Forouzanfar, M. H., Alexander, L., Anderson, H. R., Bachman, V. F., Biryukov, S., Brauer, M., Burnett, R., Casey, D., Coates, M. M., Cohen, A., Delwiche, K., Estep, K., Frostad, J. J., Astha, K. C., Kyu, H. H., Moradi-Lakeh, M., Ng, M., Slepak, E. L., Thomas, B. A., Murray, C. J. (2015). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet (London, England), 386(10010), 2287–2323. https://doi.org/10.1016/s0140-6736(15)00128-2
Ge, R., Zhang, R., & Wang, P. (2020). Prediction of chronic diseases with multi-label neural network. IEEE Access, 8, 138210-138216. https://doi.org/10.1109/ACCESS.2020.3011374
Ghosh, J. (2023). A review on understanding the risk factors for coronary heart disease in Indian college students. International Journal of Noncommunicable Diseases, 8(3), 117-128. http://dx.doi.org/10.4103/jncd.jncd_68_23
Ghosh, J., CHAUDHURI, A. N., SAHA, I., & CHAUDHURI, D. (2021). Antimicrobial Effect of 1, 25 Dihydroxy Vitamin D on Vibrio cholerae and its Association with Serum 25-Hydroxy Vitamin D Level in Rural Elderly Women: An Experimental Study. Journal of Clinical & Diagnostic Research, (10),OC34-OC37.http://dx.doi.org/10.7860/JCDR/2021/49439.15571
Ghosh, J., Chaudhuri, A. N., Saha, I., &Chaudhuri, D. (2023). Antimicrobial Effect of 1, 25 Dihydroxy Vitamin D on Escherichia coli and its Association with Serum 25 Hydroxy Vitamin D Level: An Experimental Study on the Elderly Women. Acta Scientific MICROBIOLOGY, 6(4),27-34.http://actascientific.com/ASMI/pdf/ASMI-06-1228.pdf
Ghosh, J., Chaudhuri, D., Saha, I., &Chaudhuri, A. N. (2020). Prevalence of metabolic syndrome, vitamin D level, and their association among elderly women in a rural community of West Bengal, India. Medical Journal of Dr. DY Patil University, 13(4), 315-320. http://dx.doi.org/10.4103/mjdrdypu.mjdrdypu_229_19
Ghosh, J., Chaudhuri, D., Saha, I., &Chaudhuri, A. N. (2021). Period of sun exposure and vitamin d status among the rural elderly women of West Bengal, India. Indian Journal of Community Medicine, 46(2), 285-287. https://doi.org/10.4103/ijcm.ijcm_764_20
Ghosh, J., Chaudhuri,D., Saha, I., &Chaudhuri, A.N.(2022.Prevalence of Anemia and its Association with Metabolic Syndrome and its Components Among Rural Elderly Women of Amdanga block, North 24th PGS West Bengal. Indian Journal of Gerontology, 36(2),148-158. http://dx.doi.org/10.1016/S2352-4642(23)00316-4
Ghosh, J., Singh, K., Choudhury, S. R., Koner, S., Basu, N., &Maity, S. (2022). Impact of Diet and Nutrition on Memory T Cell Development, Maintenance and Function in the Context of Healthy Immune System. Acta Scientific NUTRITIONAL HEALTH, 6(8),142-154.https://actascientific.com/ASNH/pdf/ASNH-06-1108.pdf
Gomez, A. M., Alfonso-Cristancho, R., Orozco, J. J., Lynch, P. M., Prieto, D., Saunders, R., Roze, S., & Valencia, J. E. (2016). Clinical and economic benefits of integrated pump/CGM technology therapy in patients with type 1 diabetes in Colombia. Beneficiosclínicos y económicos de la terapia con bomba de insulinaintegrada a sistema de monitoreo continuo de glucosa en los pacientesdiabéticostipo 1 en Colombia. Endocrinologia y nutricion :organo de la Sociedad Espanola de Endocrinologia y Nutricion, 63(9), 466–474. https://doi.org/10.1016/j.endoen.2016.10.009
Grundy, S. M. (2008). Metabolic syndrome pandemic. Arteriosclerosis, thrombosis, and vascular biology, 28(4), 629-636.https://doi.org/10.1161/ATVBAHA.107.151092
Grundy, S. M., Hansen, B., Smith Jr, S. C., Cleeman, J. I., Kahn, R. A., & Conference Participants. (2004). Clinical management of metabolic syndrome: report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association conference on scientific issues related to management. Circulation, 109(4), 551-556. https://doi.org/10.1161/01.CIR.0000112379.88385.67
Guillén, A., Colás, J., Fico, G., &Guillén, S. (2011). METABO: A new paradigm towards diabetes disease management. An innovative business model. In 2011 Annual international conference of the IEEE engineering in medicine and biology society (pp. 3554-3557). IEEE. https://doi.org/10.1109/IEMBS.2011.6090592
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410. http://dx.doi.org/10.1001/jama.2016.17216
Hazlehurst, B. L., Lawrence, J. M., Donahoo, W. T., Sherwood, N. E., Kurtz, S. E., Xu, S., & Steiner, J. F. (2014). Automating assessment of lifestyle counseling in electronic health records. American journal of preventive medicine, 46(5), 457-464. http://dx.doi.org/10.1115/1.4058106
Herrero, P., Bondia, J., Adewuyi, O., Pesl, P., El-Sharkawy, M., Reddy, M., Toumazou, C., Oliver, N., & Georgiou, P. (2017). Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra-day variability. Computer methods and programs in biomedicine, 146, 125–131. https://doi.org/10.1016/j.cmpb.2017.05.010
Herrero, P., Pesl, P., Reddy, M., Oliver, N., Georgiou, P., &Toumazou, C. (2014). Advanced insulin bolus advisor based on run-to-run control and case-based reasoning. IEEE journal of biomedical and health informatics, 19(3), 1087-1096.https://doi.org/10.1109/JBHI.2014.2331896
Hidalgo, J. I., Maqueda, E., Risco-Martín, J. L., Cuesta-Infante, A., Colmenar, J. M., & Nobel, J. (2014). glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes. Journal of biomedical informatics, 48, 183-192. https://doi.org/10.1016/j.jbi.2013.12.015
Hu, Y., Gui, Z., Wang, J., & Li, M. (2022). Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation. International Journal of Geographical Information Science, 36(4), 799-821. https://doi.org/10.1080/13658816.2021.1968407
Ijaz, M. F., Attique, M., & Son, Y. (2020). Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors, 20(10), 2809. https://doi.org/10.3390/s20102809
Jacobs, P. G., Resalat, N., El Youssef, J., Reddy, R., Branigan, D., Preiser, N., ... & Castle, J. (2015). Incorporating an exercise detection, grading, and hormone dosing algorithm into the artificial pancreas using accelerometry and heart rate. Journal of diabetes science and technology, 9(6), 1175-1184. https://doi.org/10.1177/1932296815609371
Jasner, Y., Belogolovski, A., Ben-Itzhak, M., Koren, O., &Louzoun, Y. (2021). Microbiome preprocessing machine learning pipeline. Frontiers in Immunology, 12, 677870. https://doi.org/10.3389/fimmu.2021.677870
Jiang, Fei& Jiang, Yong &Zhi, Hui& Dong, Yi & Li, Hao& Ma, Sufeng& Wang, Yilong& Dong, Qiang&Shen, Haipeng& Wang, Yongjun. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
Jiang, L., Qiu, B., Liu, X., Huang, C., & Lin, K. (2020). DeepFood: food image analysis and dietary assessment via deep model. IEEE Access, 8, 47477-47489. https://doi.org/10.1109/ACCESS.2020.2973625
Kaabouch, N., Hu, W. C., Chen, Y., Anderson, J. W., Ames, F., & Paulson, R. (2010). Predicting neuropathic ulceration: analysis of static temperature distributions in thermal images. Journal of biomedical optics, 15(6), 061715-061715.https://doi.org/10.1117/1.3524233
KamelBoulos, M. N., Peng, G., &VoPham, T. (2019). An overview of GeoAI applications in health and healthcare. International journal of health geographics, 18(1), 7.https://doi.org/10.1186/s12942-019-0171-2
Kathirvel, S., & Thakur, J. S. (2018). Sustainable development goals and noncommunicable diseases: Roadmap till 2030–A plenary session of world noncommunicable diseases congress 2017. International Journal of Noncommunicable Diseases, 3(1), 3-8. http://dx.doi.org/10.1158/1538-7445.OVARIAN23-B045
Katigari, M. R., Ayatollahi, H., Malek, M., &Haghighi, M. K. (2017). Fuzzy expert system for diagnosing diabetic neuropathy. World journal of diabetes, 8(2), 80. https://doi.org/10.4239%2Fwjd.v8.i2.80
Kerasidou, A. (2020). Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bulletin of the World Health Organization, 98(4), 245. https://doi.org/10.2471%2FBLT.19.237198
Khooban, M. H., ABADI, D. N., Alfi, A., &Siahi, M. (2013). Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model. Turkish Journal of Electrical Engineering and Computer Sciences, 21(7), 2110-2126. http://dx.doi.org/10.1109/IranianCEE.2012.6292429
Kopylova, E., Noé, L., &Touzet, H. (2012). SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28(24), 3211-3217. https://doi.org/10.1093/bioinformatics/bts611
Krittanawong, C. (2018). The rise of artificial intelligence and the uncertain future for physicians. European journal of internal medicine, 48, e13-e14. https://doi.org/10.1016/j.ejim.2017.06.017
Kumari, V. A., &Chitra, R. (2013). Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications, 3(2), 1797-1801. http://dx.doi.org/10.1158/1538-7445.OVARIAN23-B058
Lambert, S. A., Gil, L., Jupp, S., Ritchie, S. C., Xu, Y., Buniello, A., ... & Inouye, M. (2021). The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nature Genetics, 53(4), 420-425. http://dx.doi.org/10.1038/s41588-021-00783-5
LaPierre, N., Ju, C. J. T., Zhou, G., & Wang, W. (2019). MetaPheno: a critical evaluation of deep learning and machine learning in metagenome-based disease prediction. Methods, 166, 74-82. https://doi.org/10.1016/j.ymeth.2019.03.003
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., &Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature communications, 11(1), 3852. http://dx.doi.org/10.2337/db21-47-OR
Li, J., Huang, J., Zheng, L., & Li, X. (2020). Application of artificial intelligence in diabetes education and management: present status and promising prospect. Frontiers in public health, 8, 521222. https://doi.org/10.3389/fpubh.2020.00173
Liang, Q., Bible, P. W., Liu, Y., Zou, B., & Wei, L. (2020). DeepMicrobes: taxonomic classification for metagenomics with deep learning. NAR Genomics and Bioinformatics, 2(1), lqaa009. https://doi.org/10.1093/nargab/lqaa009 Published: 19 February 2020 Article history
Lotfata, A. (2022). Using geographically weighted models to explore obesity prevalence association with air temperature, socioeconomic factors, and unhealthy behavior in the USA. Journal of Geovisualization and Spatial Analysis, 6(1), 14. http://dx.doi.org/10.1007/s41651-022-00108-y
Luo, H., Yang, D., Barszczyk, A., Vempala, N., Wei, J., Wu, S. J., Zheng, P. P., Fu, G., Lee, K., &Feng, Z. P. (2019). Smartphone-Based Blood Pressure Measurement Using Transdermal Optical Imaging Technology. Circulation. Cardiovascular imaging, 12(8), e008857. https://doi.org/10.1161/CIRCIMAGING.119.008857
Maharana, A., &Nsoesie, E. O. (2018). Use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity. JAMA network open, 1(4), e181535-e181535..https://doi.org/10.1001/jamanetworkopen.2018.1535
Mahé, F., Rognes, T., Quince, C., de Vargas, C., &Dunthorn, M. (2014). Swarm: robust and fast clustering method for amplicon-based studies. PeerJ, 2, e593.https://doi.org/10.7717/peerj.593
Mani, S., Chen, Y., Elasy, T., Clayton, W., & Denny, J. (2012). Type 2 diabetes risk forecasting from EMR data using machine learning. In AMIA annual symposium proceedings (Vol. 2012, p. 606). American Medical Informatics Association. http://dx.doi.org/10.1158/1538-7445.AM2024-1831
Mauseth, R., Hirsch, I. B., Bollyky, J., Kircher, R., Matheson, D., Sanda, S., &Greenbaum, C. (2013). Use of a “fuzzy logic” controller in a closed-loop artificial pancreas. Diabetes technology & therapeutics, 15(8), 628-633. https://doi.org/10.1089/dia.2013.0036
Mauseth, R., Lord, S. M., Hirsch, I. B., Kircher, R. C., Matheson, D. P., &Greenbaum, C. J. (2015). Stress testing of an artificial pancreas system with pizza and exercise leads to improvements in the system’s fuzzy logic controller. Journal of diabetes science and technology, 9(6), 1253-1259. https://doi.org/10.1177/1932296815602098
Mauseth, R., Wang, Y., Dassau, E., Kircher, R., Jr, Matheson, D., Zisser, H., Jovanovic, L., & Doyle, F. J., 3rd (2010). Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor. Journal of diabetes science and technology, 4(4), 913–922. https://doi.org/10.1177/193229681000400422
Miao, F., Wen, B., Hu, Z., Fortino, G., Wang, X. P., Liu, Z. D., Tang, M., & Li, Y. (2020). Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques. Artificial intelligence in medicine, 108, 101919. https://doi.org/10.1016/j.artmed.2020.101919
Mieth, B., Rozier, A., Rodriguez, J. A., Höhne, M. M., Görnitz, N., & Müller, K. R. (2021). DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies. NAR genomics and bioinformatics, 3(3), lqab065. https://doi.org/10.1093/nargab/lqab065
Miller, J. M., Kaylor, M. B., Johannsson, M., Bay, C., &Churilla, J. R. (2014). Prevalence of metabolic syndrome and individual criterion in US adolescents: 2001–2010 National Health and Nutrition Examination Survey. Metabolic syndrome and related disorders, 12(10), 527-532. https://doi.org/10.1089/met.2014.0055
Miller, S., Nimri, R., Atlas, E., Grunberg, E. A., & Phillip, M. (2011). Automatic learning algorithm for the MD-logic artificial pancreas system. Diabetes technology & therapeutics, 13(10), 983-990. https://doi.org/10.1089/dia.2010.0216
Moghissi, E. S., Korytkowski, M. T., DiNardo, M., Einhorn, D., Hellman, R., Hirsch, I. B., Inzucchi, S. E., Ismail-Beigi, F., Kirkman, M. S., Umpierrez, G. E., American Association of Clinical Endocrinologists, & American Diabetes Association (2009). American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes care, 32(6), 1119–1131. https://doi.org/10.2337/dc09-9029.
Mohan, S., Thirumalai, C., &Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 7, 81542-81554. https://doi.org/10.1109/ACCESS.2019.2923707
Monte-Moreno, E. (2011). Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artificial intelligence in medicine, 53(2), 127-138. https://doi.org/10.1016/j.artmed.2011.05.001
Mottillo, S., Filion, K. B., Genest, J., Joseph, L., Pilote, L., Poirier, P., ... & Eisenberg, M. J. (2010). The metabolic syndrome and cardiovascular risk: a systematic review and meta-analysis. Journal of the American College of Cardiology, 56(14), 1113-1132.https://doi.org/10.1016/j.jacc.2010.05.034
Noble, D., Mathur, R., Dent, T., Meads, C., &Greenhalgh, T. (2011). Risk models and scores for type 2 diabetes: systematic review. Bmj, 343. https://doi.org/10.1136/bmj.d7163
Oh, M., & Zhang, L. (2020). DeepMicro: deep representation learning for disease prediction based on microbiome data. Scientific reports, 10(1), 6026. https://doi.org/10.1038/s41598-020-63159-5
Oka, R., Nomura, A., Yasugi, A., Kometani, M., Gondoh, Y., Yoshimura, K., &Yoneda, T. (2019). Study protocol for the effects of artificial intelligence (AI)-supported automated nutritional intervention on glycemic control in patients with type 2 diabetes mellitus. Diabetes Therapy, 10, 1151-1161. https://doi.org/10.1007/s13300-019-0595-5
Panigrahi, R., Borah, S., Bhoi, A. K., Ijaz, M. F., Pramanik, M., Jhaveri, R. H., &Chowdhary, C. L. (2021). Performance assessment of supervised classifiers for designing intrusion detection systems: a comprehensive review and recommendations for future research. Mathematics, 9(6), 690. https://doi.org/10.3390/math9060690
Panigrahi, R., Borah, S., Bhoi, A. K., Ijaz, M. F., Pramanik, M., Kumar, Y., &Jhaveri, R. H. (2021). A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets. Mathematics, 9(7), 751. https://doi.org/10.3390/math9070751
Pańkowska, E., Błazik, M., &Groele, L. (2012). Does the fat-protein meal increase postprandial glucose level in type 1 diabetes patients on insulin pump: the conclusion of a randomized study. Diabetes technology & therapeutics, 14(1), 16-22. https://doi.org/10.1089/dia.2011.0083
Park, D., Kim, K., Kim, S., Spranger, M., & Kang, J. (2021). FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings. Scientific reports, 11(1), 931. https://doi.org/10.1038/s41598-020-79422-8
Park, K. H., Pham, V. H., Davagdorj, K., Munkhdalai, L., &Ryu, K. H. (2021, April). A subtype classification of hematopoietic cancer using machine learning approach. In Asian Conference on Intelligent Information and Database Systems (pp. 113-121). Singapore: Springer Singapore. http://dx.doi.org/10.1007/978-981-16-1685-3_10
Pesl, P .,Herrero, P., Reddy, M., Xenou, Maria., Oliver, Nick., Johnston, Desmond ., Toumazou, Christofer., Georgiou, P. (2015). An advanced bolus calculator for type 1 diabetes: system architecture and usability results. IEEE journal of biomedical and health informatics, 20(1), 11-17. https://doi.org/10.1109/JBHI.2015.2464088
Pesl, P., Herrero, P., Reddy, M., Oliver, N., Johnston, D. G., Toumazou, C., & Georgiou, P. (2017). Case-based reasoning for insulin bolus advice: evaluation of case parameters in a six-week pilot study. Journal of diabetes science and technology, 11(1), 37-42. https://doi.org/10.1177/1932296816629986
Picone, D. S., Deshpande, R. A., Schultz, M. G., Fonseca, R., Campbell, N. R. C., Delles, C., Hecht Olsen, M., Schutte, A. E., Stergiou, G., Padwal, R., Zhang, X. H., & Sharman, J. E. (2020). Nonvalidated Home Blood Pressure Devices Dominate the Online Marketplace in Australia: Major Implications for Cardiovascular Risk Management. Hypertension (Dallas, Tex.: 1979), 75(6), 1593–1599. https://doi.org/10.1161/HYPERTENSIONAHA.120.14719
Pinto, Mauro & Oliveira, Hugo & Batista, Sonia & Cruz, Luis & Pinto, Mafalda&Correia, Inês& Martins, Pedro & Teixeira, César. (2020). Prediction of disease progression and outcomes in multiple sclerosis with machine learning. Scientific reports, 10(1), 21038. https://doi.org/10.1038/s41598-020-78212-6
Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., &Areán, P. A. (2019). The accuracy of passive phone sensors in predicting daily mood. Depression and anxiety, 36(1), 72-81. https://doi.org/10.1002/da.22822
Quinlan, J. R. (1981). Discovering rules by induction from collections of examples. Expert System in the Micro-Electronic Age, 168-201. https://doi.org/10.1016/0020-7373(92)90055-P
Quinlan, J. R. (1993). Program for machine learning. C4. 5. https://doi.org/10.1007/BF00993309
Reddy, M., Pesl, P., Xenou, M., Toumazou, C., Johnston, D., Georgiou, P., Herrero, P., & Oliver, N. (2016). Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study. Diabetes technology & therapeutics, 18(8), 487–493. http://dx.doi.org/10.1089/dia.2015.0413
Rigla, M., Martínez-Sarriegui, I., García-Sáez, G., Pons, B., & Hernando, M. E. (2018). Gestational diabetes management using smart mobile telemedicine. Journal of diabetes science and technology, 12(2), 260-264. https://doi.org/10.1177/1932296817704442
Rivellese, A. A., Ventura, M. M., Vespasiani, G., Bruni, M., Moriconi, V., Pacioni, D., Sulpini, M. T., Borrelli, R., Giacco, A., &Cangemi, C. (1991). Evaluation of new computerized method for recording 7-day food intake in IDDM patients. Diabetes care, 14(7), 602–604. https://doi.org/10.2337/diacare.14.7.602
Rochlani, Y., Pothineni, N. V., Kovelamudi, S., & Mehta, J. L. (2017). Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Therapeutic advances in cardiovascular disease, 11(8), 215-225. https://doi.org/10.1177/1753944717711379
Russell, S. J., &Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson. https://thuvienso.hoasen.edu.vn/handle/123456789/8967
Sacchi, L., Dagliati, A., Segagni, D., Leporati, P., Chiovato, L., &Bellazzi, R. (2015, August). Improving risk-stratification of Diabetes complications using temporal data mining. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2131-2134). IEEE. https://doi.org/10.1109/EMBC.2015.7318810
Sharma, D., Paterson, A. D., &Xu, W. (2020). TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction. Bioinformatics, 36(17), 4544-4550. https://doi.org/10.1093/bioinformatics/btaa542
Sharma, V., Sharma, V., Khan, A., Wassmer, D. J., Schoenholtz, M. D., Hontecillas, R., Bassaganya-Riera, J., Zand, R., &Abedi, V. (2020). Malnutrition, Health and the Role of Machine Learning in Clinical Setting. Frontiers in nutrition, 7, 44. https://doi.org/10.3389/fnut.2020.00044
Singh, K., Choudhury, S. R., Ghosh, J., Koner, S., & Pal, R. (2023). Ageing and its relationship with psychoneuroimmunology. Journal of Comprehensive Health, 11(2). http://dx.doi.org/10.53553/JCH.v11i02.004
Srinivasan, A., Lee, J. B., Dassau, E., & Doyle III, F. J. (2014). Novel insulin delivery profiles for mixed meals for sensor-augmented pump and closed-loop artificial pancreas therapy for type 1 diabetes mellitus. Journal of diabetes science and technology, 8(5), 957-968. https://doi.org/10.1177/1932296814543660
Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21(8), 2852. https://doi.org/10.3390/s21082852
Steinfeldt, J., Buergel, T., Loock, L., Kittner, P., Ruyoga, G., ZuBelzen, J. U., Sasse, S., Strangalies, H., Christmann, L., Hollmann, N., Wolf, B., Ference, B., Deanfield, J., Landmesser, U., &Eils, R. (2022). Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort. The Lancet. Digital health, 4(2), e84–e94. https://doi.org/10.1016/s2589-7500(21)00249-1
Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PloS one, 12(6), e0179790. https://doi.org/10.1371/journal.pone.0179790
Uhlig, K., Patel, K., Ip, S., Kitsios, G. D., & Balk, E. M. (2013). Self-measured blood pressure monitoring in the management of hypertension: a systematic review and meta-analysis. Annals of internal medicine, 159(3), 185-194. https://doi.org/10.7326/0003-4819-159-3-201308060-00008
Usher, D., Dumskyj, M., Himaga, M., Williamson, T. H., Nussey, S., & Boyce, J. (2004). Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabetic Medicine, 21(1), 84-90. https://doi.org/10.1046/j.1464-5491.2003.01085.x
Walsh, J., Roberts, R., Weber, D., Faber-Heinemann, G., & Heinemann, L. (2015). Insulin pump and CGM usage in the United States and Germany: results of a real-world survey with 985 subjects. Journal of diabetes science and technology, 9(5), 1103-1110. https://doi.org/10.1177/1932296815588945
Wang, W., Duan, L. Y., Jiang, H., Jing, P., Song, X., &Nie, L. (2021). Market2Dish: health-aware food recommendation. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(1), 1-19. https://doi.org/10.1145/3418211
Wang, Y., Bhattacharya, T., Jiang, Y., Qin, X., Wang, Y., Liu, Y., Saykin, A. J., & Chen, L. (2021). A novel deep learning method for predictive modeling of microbiome data. Briefings in bioinformatics, 22(3), bbaa073. http://dx.doi.org/10.1093/bib/bbaa073
WHO, G. (2017). Noncommunicable diseases progress monitor. https://www.who.int/publications/i/item/9789241513029
Wiens, J., &Shenoy, E. S. (2018). Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clinical infectious diseases, 66(1), 149-153. https://doi.org/10.1093/cid/cix731
World Health Organization. (2012). Action plan for implementation of the European strategy for the prevention and control of noncommunicable diseases 2012− 2016. https://iris.who.int/handle/10665/352659
World Health Organization. (2018). Noncommunicable diseases country profiles 2018. https://www.who.int/publications/i/item/9789241514620
World Health Organization. Nutrition topics:Nutrition for Older persons. WHO, 2021. https://www.who.int/nutrition/topics/ageing/en/.
Xiao, J., Ding, R., Xu, X., Guan, H., Feng, X., Sun, T., Zhu, S., & Ye, Z. (2019). Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. Journal of translational medicine, 17(1), 119. https://doi.org/10.1186/s12967-019-1860-0
Xu, X., Solon-Biet, S. M., Senior, A., Raubenheimer, D., Simpson, S. J., Fontana, L., Mueller, S., & Yang, J. Y. H. (2020). LC-N2G: a local consistency approach for nutrigenomics data analysis. BMC bioinformatics, 21(1), 530. https://doi.org/10.1186/s12859-020-03861-3
Yadav, J., Rani, A., & Singh, V. (2016). Performance analysis of fuzzy-PID controller for blood glucose regulation in type-1 diabetic patients. Journal of medical systems, 40(12), 254. https://doi.org/10.1007/s10916-016-0602-6
Yap, M. H., Chatwin, K. E., Ng, C. C., Abbott, C. A., Bowling, F. L., Rajbhandari, S., Boulton, A. J. M., & Reeves, N. D. (2018). A New Mobile Application for Standardizing Diabetic Foot Images. Journal of diabetes science and technology, 12(1), 169–173. https://doi.org/10.1177/1932296817713761
Yu, W., Liu, T., Valdez, R., Gwinn, M., &Khoury, M. J. (2010). Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC medical informatics and decision making, 10, 1-7.https://doi.org/10.1186/1472-6947-10-16
Yun, T., Li, H., Chang, P. C., Lin, M. F., Carroll, A., & McLean, C. Y. (2020). Accurate, scalable cohort variant calls using DeepVariant and GLnexus. Bioinformatics, 36(24), 5582-5589. https://doi.org/10.1093/bioinformatics/btaa1081
Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalová L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001
Zhang, S., Yao, L., Sun, A., &Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR), 52(1), 1-38. http://dx.doi.org/10.1145/3285029
Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S., &Delp, E. J. (2010). The use of mobile devices in aiding dietary assessment and evaluation. IEEE journal of selected topics in signal processing, 4(4), 756-766. https://doi.org/10.1109/JSTSP.2010.2051471
Zhu, Q., Jiang, X., & Zhu, Q. (2019). Graph embedding deep learning guides microbial biomarkers' identification. Frontiers in genetics, 10, 491009. https://doi.org/10.3389/fgene.2019.01182
Statistics
1159 Views | 674 Downloads
How to Cite
Ghosh, J., Roy Choudhury, S., Singh, K., & Koner, S. (2024). Application of Machine Learning Algorithm and Artificial Intelligence in Improving Metabolic Syndrome related complications: A review. International Journal of Advancement in Life Sciences Research, 7(2), 41-67. https://doi.org/https://doi.org/10.31632/ijalsr.2024.v07i02.004
Section
Review Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
.