AI-Powered Early Detection of Diabetes Using Machine Learning on Electronic Health Records

Millions of people worldwide suffer from diabetes, a chronic illness that must be identified early in order to be effectively managed and complications avoided. Conventional diagnostic techniques depend on recurring clinical evaluations, which could postpone prompt action. This study investigates the use of machine learning (ML) methods for early diabetes detection in electronic health records (EHRs). To increase predictive accuracy, we offer an optimized machine learning framework that makes use of the patient's medical history, test results, and lifestyle choices. Results from experiments show that ML models perform better than traditional diagnostic techniques in terms of overall predictive performance, sensitivity, and specificity. Lastly, we go over potential future paths, such as combining explainable AI and deep learning to improve decision-making.