Title: Machine learning-based framework for early prediction of diabetes

Authors: Salliah Shafi Bhat; Venkatesan Selvam; Gufran Ahmad Ansari

Addresses: B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai-48, India ' B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai-48, India ' Faculty of Science, MIT World Peace University (MIT-WPU), Pune- 411 038, India

Abstract: The recent advancements in technology have changed the landscape of healthcare. With changes in lifestyle and rise in living standard diabetes remains the leading cause of death globally. Prediction of diabetes using machine learning algorithms (MLA) for early prediction is the need of the hour. However, it is still in its nascent stage. The goal of this study is to employ significant features of machine learning algorithms to the prediction of diabetic and to get the best results which are compared to clinical outcomes. Using predictive analysis, the suggested strategy focuses on choosing the features that aid in the early detection of diabetes. The result shows that the support vector machine (SVM) algorithm has the highest accuracy of 99.349% as compared to naive Bayes which is 98.95%. In order to improve classification and accuracy, this research also normalises the selection of suitable features in the data.

Keywords: machine learning; naive Bayes; support vector machine; SVM; prediction; diabetes; patients.

DOI: 10.1504/IJMEI.2025.145851

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.3, pp.219 - 231

Received: 10 Mar 2022
Accepted: 19 Aug 2022
Published online: 30 Apr 2025 *

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