AI-Powered Web Application for Early Prediction of Diabetes and Associated Chronic Diseases

Chronic diseases such as diabetes, heart disease, chronic kidney disease, and lung disorders require continuous monitoring and early risk prediction to prevent severe complications and reduce healthcare burden. Traditional healthcare systems rely on periodic clinical visits and manual analysis of laboratory reports, which often delay early detection and intervention. This paper proposes an AI-Powered Web Application for Early Prediction of Diabetes and Associated Chronic Diseases that integrates machine learning and deep learning techniques to provide real-time predictive insights. The system analyzes structured clinical parameters such as glucose levels, blood pressure, cholesterol, BMI, and lifestyle indicators, along with unstructured medical data such as scanned laboratory reports and diagnostic images. The application enables users to receive instant disease risk predictions, personalized health recommendations, and second-opinion support through an interactive web interface developed using Streamlit. Secure authentication and cloud- based storage mechanisms ensure data privacy and accessibility. By facilitating early diagnosis, continuous monitoring, and preventive healthcare awareness, the proposed system improves healthcare accessibility, especially for individuals in rural and remote regions.