AI-Powered Cardiac Risk Prediction System Using Random Forest and Decision Tree

Despite advances in clinical medicine, cardiovascular disease among the primary causes of mortality is still illness. Worldwide, killing one person every minute due to complications stemming from heart disease. The high velocity of life today makes it difficult to catch heart disease early. Machine Learning (ML) holds great potential in diagnosing diseases accurately and early in health care.This revision presents A condition of the heart prediction system that utilizes patient medical records and applies particular Random Forest, Decision Tree, and machine learning algorithms for analysis, containing various clinical parameters, is processed in Python, enabling prediction of cardiac conditions based on historical patient data. Among the models used, the Random Forest algorithm demonstrates superior reliability and early detection cardiac stages. The proposed system reads patient data from CSV files and effectively determines the likelihood of a heart attack. It offers advantages such as high performance, flexibility, and a high success rate in prediction.