Heart Disease Prediction Using Machine Learning Algorithms

Abstract

Heart disease is one of the major causes of death across the world, which makes early diagnosis extremely important. This project focuses on developing a prediction system using machine learning techniques to identify the risk of heart disease in patients. The system analyzes medical and demographic data such as age, cholesterol level, blood pressure, and heart rate.Different machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are applied to study patterns within the dataset. The effectiveness of these machine learning models is measured using evaluation metrics such as accuracy, precision, recall, and F1-score to understand how well the models predict heart disease. Among the tested algorithms, ensemble methods like Random Forest usually provide better accuracy and stability.The developed system is intended to support healthcare professionals in diagnosing heart disease more quickly and effectively. By assisting doctors in early detection, the system can help improve patient health outcomes and reduce medical costs.