Diabetes Prediction Using Machine Learning

Diabetes mellitus is a chronic metabolic disorder that affects a large and growing population worldwide. Early detection and continuous monitoring are essential to prevent severe complications associated with the disease. Traditional diagnostic and monitoring methods are often periodic and lack continuous patient engagement. Recent advancements in machine learning and artificial intelligence provide effective solutions for predictive analysis and personalized healthcare support. This project presents a Diabetes Prediction System using Machine Learning techniques implemented in Python, integrated with a web-based frontend dashboard and an intelligent chatbot. The system employs supervised machine learning algorithms to analyse patient health parameters such as glucose level, insulin level, body mass index (BMI), age, and other relevant attributes to predict the risk of diabetes. The frontend interface enables users to log and visualize blood glucose and insulin levels through interactive charts. An AI-powered chatbot assists patients by providing personalized diet recommendations, reminders for glucose and insulin monitoring, and alerts during abnormal readings. The proposed system aims to improve early diagnosis, enhance self-management, and reduce diabetes-related complications. This integrated solution demonstrates the potential of intelligent healthcare systems in preventive medicine and patient-centric care.