HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES

Heart disease is one of the most life-threatening conditions worldwide, responsible for a significant number of deaths each year.Early detection and preventive healthcare can greatly reduce mortality rates.This paper presents a Machine Learning-based system for predicting heart disease by analyzing clinical data such as age, cholesterol, blood pressure, and glucose levels.Various algorithms such as Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are implemented and compared to determine the most accurate predictive model.The system leverages Python-based tools for data preprocessing, model training, and evaluation.Results indicate that Random Forest achieves the highest accuracy, providing an efficient and reliable decision-support tool for medical practitioners.This approach can significantly assist in early diagnosis, personalized treatment, and healthcare automation.