AI-Powered Predictive Models for Early Diagnosis of Chronic Diseases Using EHR Data

Chronic diseases represent a significant burden on global healthcare systems due to their long-term nature and high treatment costs. Early diagnosis is critical in managing these diseases effectively. This research proposes the development and evaluation of AI-powered predictive models leveraging electronic health record (EHR) data to enable early detection of chronic conditions such as diabetes, cardiovascular disease, and chronic kidney disease. Using a combination of logistic regression, random forest, and deep learning models, the study analyzes a large EHR dataset to identify early indicators and patterns predictive of chronic diseases. The models are evaluated using accuracy, precision, recall, F1 score, and AUC-ROC metrics. Results demonstrate that ensemble and deep learning models significantly outperform traditional models, achieving high diagnostic accuracy. The findings emphasize the potential of AI-based diagnostics in transforming chronic disease management and underscore the importance of integrating such systems into healthcare infrastructures. Keywords: Chronic Disease, EHR, Predictive Models, Artificial Intelligence, Machine Learning, Early Diagnosis