Predictive Modeling and Comprehensive Analysis of Cardiovascular Disease Risk Factors

Cardiovascular disease is one of the largest ongoing causes of death worldwide. The estimation and early detection of risk factors for CVD can be done effectively to reduce its impact. This research focuses on building predictive models for the determination of cardiovascular disease risk factors through complete data analysis of important relevant risk factors, such as age, gender, smoking habits, diet, physical activity, cholesterol levels, and family history. Machine learning algorithms used include Logistic Regression, Random Forest, and Support Vector Machines for historical analysis of patient data to provide a prediction of risks. Metrics used include accuracy, sensitivity, specificity, and the area under the ROC curve, or AUC. Results: The results indicated that Random Forest surpasses the other algorithms in the prediction of CVD risk. Furthermore, in providing results about the relative importance of the risk factors leading to predictive significance in CVD, this study will help healthcare professionals concentrate on which is worth prevention. Bringing predictive modeling and healthcare together will enhance patient care and subsequently support early intervention strategies toward reducing the burden of cardiovascular diseases globally.