PATIENT DISEASE RISK PREDICTION (DIABETES / HEART) USING BIG DATA ANALYTICS

The development of a Patient Disease Risk Prediction System using machine learning techniques demonstrates the growing importance of data-driven healthcare solutions in modern society. Chronic illnesses such as diabetes and cardiovascular disease continue to affect millions of people worldwide, especially in developing nations like India, where access to early diagnosis tools is still limited in many regions. These diseases often remain undetected until severe symptoms appear, leading to high treatment costs, long-term complications, and reduced quality of life. Therefore, early risk assessment systems play a vital role in preventive healthcare and patient awareness. The proposed system successfully integrates machine learning, data analytics, and web technologies into a unified platform capable of predicting the likelihood of diabetes and heart disease using patient clinical data. By applying the Random Forest algorithm, the system is able to identify hidden relationships among medical parameters such as glucose level, blood pressure, cholesterol, age, and heart rate. The trained model generates probability-based predictions that help classify patients into low, moderate, or high-risk categories. This approach improves understanding of health risks and encourages timely medical consultation, lifestyle changes, and preventive measures. In conclusion, this project highlights the potential of machine learning and big data analytics in improving early disease detection and preventive healthcare. By combining predictive modeling with an accessible web interface, the system demonstrates how intelligent technologies can assist in reducing disease risk awareness gaps and promoting healthier lifestyles.

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