Prediction of Heart Disease Using Machine Learning Algorithms

This project presents a heart disease prediction system that integrates machine learning algorithms with real-time sensor data to provide timely health alerts. The system utilizes a heartbeat sensor and a DHT11 sensor to continuously monitor vital signs such as heart rate and body temperature. Sensor data is transmitted to a machine learning model trained to detect abnormal patterns indicative of potential heart disease. Upon identifying abnormal readings, the system communicates the results back to an Arduino microcontroller. In response, the Arduino triggers a buzzer alert, displays the warning on an LCD screen, and sends an emergency message via a GSM module to notify caregivers or medical professionals. This integrated approach enables early detection and immediate response, enhancing patient safety and healthcare efficiency.