Machine Learning for Early and Accurate Prediction of Cardiovascular Disease Risk
Simran Devi1, Sagar Choudhary2
Cardiovascular Diseases (CVDs) remain the leading cause of global mortality. Timely and accurate diagnosis is crucial for effective intervention and improved patient prognosis. This paper investigates the utility of advanced Machine Learning (ML) techniques—specifically Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Networks (DNN)—to enhance the early prediction of CVD risk using routine patient health metrics (e.g., age, cholesterol levels, blood pressure, BMI). We analyzed a publicly available clinical dataset of N patients. The results demonstrate that the DNN model achieved the highest predictive performance, with an accuracy of 91.2% and an F1-score of 90.5%, significantly outperforming traditional statistical models and shallower ML methods. This study highlights the immense potential of ML models as a robust decision-support tool for clinicians in proactive CVD management.