Diabetes Prediction System Using Machine Learning

Diabetes is a prevalent chronic condition affecting millions worldwide, and timely detection is essential to prevent severe complications. This study presents a webbased diabetes prediction system developed using the Django framework, integrating machine learning techniques for accurate risk assessment. The system analyzes key health parameters, including glucose levels, blood pressure, body mass index (BMI), insulin, skin thickness, and age, to evaluate an individual’s likelihood of developing diabetes. Users can obtain immediate risk predictions through a Random Forest model and interact with a chatbot designed to provide informative responses to diabetesrelated queries. The combined use of predictive analytics and conversational AI enhances user engagement, awareness, and accessibility. Experimental evaluation demonstrates that the system delivers reliable early risk detection, highlighting the potential of AI-driven healthcare solutions for proactive monitoring and patient education.