Abstract
The escalating burden of cardiovascular diseases (CVDs) necessitates advanced diagnostic tools for timely intervention. This study comprehensively evaluates the efficacy of various machine learning (ML) algorithms in the early detection of CVDs. Leveraging a large, anonymized dataset comprising clinical features, patient history, and diagnostic markers, we implemented and assessed several ML models, including Support Vector Machines (SVM), Random Forests (RF), Logistic Regression (LR), and Gradient Boosting Machines (GBM). Our methodology focused on rigorous performance evaluation using standard metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Results indicate that ensemble methods, particularly Random Forests and Gradient Boosting Machines, demonstrated superior predictive performance compared to traditional models like Logistic Regression. Specifically, RF achieved an accuracy of 92.5% and an AUC of 0.95, while GBM achieved 91.8% accuracy and an AUC of 0.94. These findings underscore the significant potential of ML algorithms, especially ensemble techniques, to enhance the early detection of CVDs. Further validation on diverse clinical populations is warranted to translate these findings into routine clinical practice, thereby improving patient outcomes and reducing healthcare costs. The study contributes to the growing body of evidence supporting ML's role in precision medicine for cardiovascular health.