STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews: A Journal of Bioinformatics Original Research

Enhanced Diabetes Prediction: A Comparative Study of Machine Learning Models

  1. Md. Nuzmul Hossain Nahid Department of CSE, Dhaka International University
  2. Md. Abdul Based Department of CSE, Dhaka International University

Abstract

Excessively high blood glucose levels lead to diabetes, a condition that can be better managed with early detection, resulting in a longer life and improved health. Machine learning models are essential tools in diagnosing diabetes, especially when trained on appropriate and relevant datasets. In this study, a combination of ensemble methods and nine distinct machine learning algorithms were utilized to develop a predictive model for diabetes diagnosis based on a publicly accessible dataset. Among the models tested, the Random Forest algorithm demonstrated superior performance, achieving the highest prediction accuracy of 99.75%. This highlights the effectiveness of ensemble-based approaches in enhancing diagnostic precision and underscores the potential of machine learning in supporting clinical decision-making for diabetes detection. The study emphasizes the value of data-driven techniques in improving the early identification and management of diabetes. A comparison with existing studies highlights the strength and superiority of our approach. Additionally, a user-friendly web application has been developed using the best-performing model, providing users with diabetes predictions and relevant educational videos.

Keywords

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