Gender-Specific Lifestyle Risk Behaviors and Machine Learning Models for Predicting Atherosclerotic Cardiovascular Disease

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of global morbidity and mortality. We aimed to assess the predictive accuracy of machine learning (ML) models incorporating lifestyle risk behaviors for ASCVD risk stratified by gender, using data from the Korea National Health and Nutrition Examination Survey. We analyzed data from 8,573 participants aged 40–79 years, excluding those with prior cardiovascular events. ASCVD risk was assessed using the American College of Cardiology/American Heart Association Pooled Cohort Equations, with a high-risk threshold of ≥ 15% over 10 years. Five ML algorithms—logistic regression (LR), support vector machine, random forest, extreme gradient boost, and light gradient boosting models—were utilized, with performance metrics including AUROC, accuracy, precision, recall, and F1 score. Among men, the support vector machine model achieved the highest AUROC of 0.952, whereas, among women, the LR model achieved the highest AUROC of 0.980. Significant predictors for men included age, smoking, BMI, and LDL cholesterol, while for women, predictors extended to household income and residential area. Comparing the significant the Shapley additive explanation variables in the ML model to the significant variables in the conventional bivariate LR model, lifestyle risk behaviors such as household income, residential area, and weight change over 1 year were identified as significant variables in both models. This analysis provides the importance of gender-specific lifestyle risk factors in ASCVD prediction. The integration of ML and lifestyle factors offers enhanced predictive capabilities over traditional models, highlighting the necessity for tailored prevention strategies.