Diabetic Risk Prediction Based on Medical and Lifestyle Patterns

Diabetes mellitus is a common chronic condition that creates significant challenges for public health. Early risk prediction can help with timely prevention and better self-management. Many studies focus only on clinical data, but fewer consider lifestyle and behavioural signals that greatly affect disease onset. This work introduces a machine-learning web application that estimates a person’s risk of diabetes by looking at clinical factors (age, BMI, HbA1c, blood pressure, family history) and lifestyle habits (diet, physical activity, stress, sleep quality, and screen time). The system is built on the MERN stack to ensure secure data storage, user authentication, and interactive visualizations. Our model classifies users into Low, Moderate, or High-risk categories and offers practical, personalized advice for diet, exercise, and stress management. Experiments showed that combining lifestyle factors with medical features improved prediction accuracy compared to using clinical data alone, with analysis showing added value from lifestyle variables. These findings highlight the potential of digital health tools that mix medical and everyday data to help people make informed choices. We include basic protections like consent, role-based access, and anonymized storage to safeguard user privacy