Early Prediction of Heart Diseases Using Machine Learning Algorithms-A Review

The project titled “Early Prediction of Heart Diseases Using Machine Learning Algorithms – A Review” focuses on integrating embedded hardware with intelligent prediction models to identify potential heart-related abnormalities at an early stage. The system employs an Arduino microcontroller to collect real-time physiological data from biomedical sensors, including a heartbeat sensor for measuring pulse rate, a temperature sensor for monitoring body temperature, and a pulse oximeter for detecting SpO₂ levels. These sensor readings are displayed on an LCD and continuously monitored to identify abnormal variations. In critical conditions, a buzzer is activated to provide immediate alerts. The acquired data is transmitted to a Python-based system, where a machine learning model—specifically the Random Forest algorithm—is implemented to analyze and predict the likelihood of heart disease based on historical and real-time data. The prototype effectively demonstrates how embedded systems combined with machine learning can provide a low-cost, portable, and efficient solution for early detection and preventive healthcare, especially in remote or resource-limited environments.