Assessing Machine Learning Methods for Predicting Diabetes among Pregnant Women

Zahura Zaman; Md. Ashrak Al Arif Shohas; Mahedi Hasan Bijoy; Meherab Hossain; Shakawat Al Sakib

doi:10.31632/ijalsr.2022.v05i01.005

Zahura Zaman Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh https://orcid.org/0000-0001-5265-7643
Md. Ashrak Al Arif Shohas Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh https://orcid.org/0000-0001-7981-6034
Mahedi Hasan Bijoy Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh https://orcid.org/0000-0003-0875-5718
Meherab Hossain Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh https://orcid.org/0000-0002-6245-445X
Shakawat Al Sakib Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh https://orcid.org/0000-0003-3130-4026

DOI https://doi.org/10.31632/ijalsr.2022.v05i01.005

Abstract

Machine Learning has a big impact on a lot of different scientific and technical disciplines, including medical research and Biophysics. Diabetes is a chronic condition marked by abnormally high glucose levels and the body's inefficient utilization of insulin. Diabetes is now becoming a leading cause of death all over the world. The objective of this article was to use multiple. Machine Learning methods are used to create a model with a limited number of dependencies, which could be used to study diabetic patients and diagnose diabetes using the PIMA dataset. Some of the most well-known prediction algorithms employed in this system are SVM (support vector machine), Multinomial Naive Bayes, Random forest, and Decision tree. Use these algorithms to construct a gathering of models by combining multiple combinations into one. This will enhance performance and accuracy.

Keywords: Diabetes prediction, dataset of PIMA, train test split, machine learning, classification

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