Heart Disease Predication Using Machine Learning

Heart-related diseases or Cardiovascular Diseases (CVDs) have been the leading cause of death globally. There is a need for a reliable and accurate system to diagnose them early. This research explores machine learning algorithms such as Random Forest, SVM, and hybrid models to predict heart disease using the UCI dataset. The system uses key medical parameters like cholesterol, heart rate, and chest pain type. Among the tested models, Random Forest achieved the best performance with high accuracy and AUC score, offering a promising tool for early diagnosis.