Cardiovascular Disease Classification Using Machine Learning Techniques

Cardiovascular disease (CVD) remains a major global health burden, motivating the development of accurate and scalable risk screening methods. This study presents a comparative evaluation of supervised machine learning models for CVD prediction using routinely collected clinical and lifestyle data. A unified machine learning pipeline was constructed, incorporating data preprocessing, feature transformation, and systematic model tuning. Multiple classifiers were evaluated under identical experimental settings, including logistic regression, support vector machine, multilayer perceptron, and ensemblebased tree models. Experimental results on a large public dataset show that ensemble methods consistently outperform classical classifiers. In particular, XGBoost achieves the best overall performance, with an accuracy of 87.02%, an AUC of 94.83%, and an F1-score of 8 5. 1 0%. These results highlight the potential of gradient-boosting-based machine learning models to support accurate and scalable cardiovascular disease risk screening using routinely collected health data.