Leveraging Machine Learning Algorithms for Predictive Detection of Chronic Diseases

Chronic diseases such as diabetes, cardiovascular disorders, and hypertension contribute significantly to global morbidity and healthcare costs, often due to delayed diagnosis and limited early-warning mechanisms. With the increasing availability of electronic health records, wearable sensor data, and population health datasets, machine learning has emerged as a promising tool for early and accurate prediction of these long-term illnesses. This study explores the application of various supervised and ensemble learning algorithms, including Random Forest, Support Vector Machines, and Gradient Boosting, to identify patterns and risk factors that could indicate the onset of chronic conditions. The research focuses on evaluating their predictive efficiency, interpretability, and suitability for large-scale clinical deployment. Results reveal that ensemble models demonstrate superior performance due to their ability to manage complex and imbalanced datasets, while model interpretability remains critical for clinical trust and adoption. The findings underscore the potential of machine learning models not only for risk stratification but also for supporting personalized healthcare planning. The study highlights the importance of integrating domain knowledge, explainable AI methods, and privacy-preserving techniques for responsible and sustainable use in real-world medical environments.