Abstract

Chronic diseases such as diabetes, cardiovascular conditions, and chronic kidney disease are major global health concerns. Early detection of these conditions significantly improves patient outcomes and reduces healthcare costs. This paper explores the development and implementation of artificial intelligence (AI) algorithms to identify early signs of chronic diseases using electronic health records (EHRs) and patient-generated health data. By applying machine learning models such as decision trees, support vector machines, and deep learning neural networks, we demonstrate improved prediction accuracy for disease onset. Our approach also integrates feature engineering techniques and interpretable AI to enhance clinical applicability.