Heart Disease Prediction Using Machine Learning

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, necessitating the development of highly accurate and efficient early detection systems. This study proposes a robust machine learning framework designed to predict the likelihood of heart disease in patients based on clinical and behavioral risk factors. Utilizing comprehensive health datasets containing critical attributes such as age, blood pressure, cholesterol levels, and electrocardiogram results, the research explores various preprocessing techniques, including missing value imputation and feature scaling. Advanced machine learning algorithms, including Support Vector Machines (SVM), Random Forest, Logistic Regression, and Extreme Gradient Boosting (XGBoost), are implemented and rigorously evaluated. Feature selection techniques are employed to identify the most significant predictors of cardiac events, thereby enhancing model interpretability and reducing computational complexity. To address potential class imbalances within the medical data, Synthetic Minority Over-sampling Technique (SMOTE) is utilized, ensuring unbiased model training. The performance of each predictive model is systematically assessed using metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic curve (AUCROC). Experimental results demonstrate that ensemble learning methods, particularly Random Forest and XGBoost, outperform traditional classifiers, achieving superior diagnostic accuracy and sensitivity. The proposed system integrates these optimized algorithms into a cohesive framework capable of assisting healthcare professionals in making data-driven clinical decisions. By enabling early intervention and personalized patient risk stratification, this machine learning approach holds significant potential to mitigate the global burden of heart disease. Ultimately, this research bridges the gap between data science and clinical cardiology, offering a scalable, non-invasive, and costeffective screening tool for modern healthcare applications