Revolutionizing Diabetes Prediction: A Machine Learning Approach to Early Detection and Intervention

Abstract

Diabetes mellitus, a chronic metabolic disorder affecting over 422 million people globally, poses significant challenges to public health due to its increasing prevalence and severe complications. This research explores the application of machine learning (ML) techniques to revolutionize diabetes prediction, aiming to enhance early detection and intervention strategies. We implemented and compared various ML algorithms, including logistic regression, decision trees, support vector machines, random forests, k-nearest neighbors, XGBoost, and artificial neural networks, using a comprehensive dataset of diabetes-related features. Data preprocessing techniques, including normalization and addressing class imbalance, were applied to improve model performance. Our results demonstrate that ensemble methods and neural networks exhibited superior predictive performance. The logistic regression model, used as a baseline, achieved an overall accuracy of 92%.

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