Heart Disease Prediction Using Machine Learning Algorithms

Cardiovascular diseases, i.e heart diseases, remain one of the major causes of death throughout the world it tolling for nearly one-third of all fatalities.Timely detection and risk assessment play a critical role in preventing lifethreatening cardiac events.However, traditional diagnostic approaches are often time-consuming, expensive, and inaccessible in many regions due to limited healthcare infrastructure.With the rapid advancement of machine learning, predictive analytics is emerging as a powerful tool for identifying individuals at heart disease risk based on their clinical results.This paper is based on a heart disease prediction system that leverages a machine learning model which is trained on the Cleveland Heart Disease dataset.The system takes thirteen medical parameters-including age, blood pressure, cholesterol levels, and ECG results-as input.It then classifies the heart disease risk using a trained Random Forest classifier, which is carefully chosen after comparative evaluation with other supervised learning algorithms.The final model serves as a preliminary risk assessment tool aimed at raising awareness and encouraging users to seek medical evaluation if necessary.While it does not replace clinical diagnostics, the application shows the potential of integrating machine learning to support preventive healthcare.