Machine Learning in Healthcare: Analyzing Performance of Algorithms for Diabetes Risk Prediction

Diabetes remains a major global health challenge, with early detection critical to minimizing complications and improving patient outcomes. Machine learning (ML) has emerged as a powerful tool for risk prediction, leveraging large and complex datasets to provide accurate and timely predictions. This paper explores the application of various ML algorithms, including decision trees, support vector machines, and deep learning models, for diabetes risk prediction. It provides a comparative analysis of algorithm performance based on metrics such as accuracy, precision, recall, and AUC-ROC, while discussing the importance of data preprocessing, feature selection, and cross-validation in optimizing results. The paper also highlights practical challenges in deploying ML models in healthcare systems, including integration with electronic health records, privacy concerns, and the need for interpretability. By synthesizing recent advancements and case studies, this work offers insights into algorithm selection and future directions for improving diabetes care using ML.