Cardiovascular Disease Diagnosing Using Machine Learning

Cardiovascular diseases (CVDs) are among the leading causes of death worldwide, making early diagnosis and continuous health monitoring essential for effective treatment. This project presents a Machine Learning-Based Cardiovascular Disease Diagnosis System using Raspberry Pi as the main controller. The system continuously monitors vital health parameters such as heart rate, pulse rate, and body temperature using a heartbeat sensor, pulse sensor, and Dallas temperature sensor. Since some sensors provide analog outputs, an ADC converter is used to convert analog signals into digital data that can be processed by the Raspberry Pi. The collected physiological data is analyzed using a machine learning algorithm trained to identify patterns associated with cardiovascular diseases. The diagnostic results and sensor readings are displayed on an LCD screen for easy monitoring. If abnormal health conditions or potential cardiovascular risks are detected, a buzzer generates an immediate alert to notify the user or healthcare provider. The proposed system offers a low-cost, portable, and intelligent healthcare solution for early disease detection, continuous patient monitoring, and improved medical decision-making. It can be effectively utilized in hospitals, clinics, and home healthcare environments to enhance cardiovascular health management.