Patient Alert System To Predict And Alert Cardiovascular Disease Risk

This project uses the Random Forest algorithm to predict heart disease risk based on key health data. Using the Cleveland heart disease dataset and chi-square feature selection, the model achieved 88.5% accuracy. It also provides real-time alerts with patient location, aiding timely medical response and better decision-making.