A Dataset-Specific Machine Learning Study for Predicting Diabetes (Type-2) in a Developing Country Context

Md Rakibul Haque; Ibraheem Alharbi

doi:10.17485/IJST/v15i38.1183

Article

A Dataset-Specific Machine Learning Study for Predicting Diabetes (Type-2) in a Developing Country Context

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i38.1183

Year: 2022, Volume: 15, Issue: 38, Pages: 1932-1940

Original Article

A Dataset-Specific Machine Learning Study for Predicting Diabetes (Type-2) in a Developing Country Context

Md Rakibul Haque¹, Ibraheem Alharbi^2*

¹MIS Dept, University of Dhaka, Bangladesh
²MIS Dept, College of Business, University of Jeddah, Kingdom of Saudi Arabia (KSA)

*Corresponding Author
Email: [email protected]

Received Date:04 June 2022, Accepted Date:17 August 2022, Published Date:14 October 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Diabetes become more prevalent across the globe, understanding their sources and causes are more important than ever. This study uses machine learning techniques to efficiently detect Diabetic patients from many features. Methods: The purpose of this paper is to conduct a dataset-specific machine learning study for predicting diabetes in Bangladesh. Classification is used with 18 features including demographic characteristics, family history, dieting habit, clinical features, physical activities, and life quality. Five different classifiers are used. Findings: Based on using five different classifiers, results suggest that the Logistic Regression performed the best in predicting diabetes for this dataset. The accuracy of the logistic regression classifier exceeds 83.8%. Novelty: Unlike other studies, the authors combine eating habits with demographic and health features to enhance the performance of the classifiers. The result suggests that while addition of factors or features related to eating habits and lifestyle can increase the accuracy of prediction, the inclusion of more clinical features is more important to increase the accuracy. The authors believe that this finding is significant in the context of developing countries like Bangladesh considering the limited health-resource available as well as the fact of fast-changing of eating habits and lifestyle. Keywords: Machine Learning; chronic disease; classification; logistic regression; and diabetes

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Copyright

© 2022 Haque & Alharbi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)