Long-term Predictors of Cardiovascular Disease: A Machine Learning Approach

Long-term Predictors of Cardiovascular Disease: A Machine Learning Approach

This study investigates long-term cardiovascular disease (CVD) risk predictors for middle-aged and older adults in Korea. Using the Least Absolute Shrinkage and Selection Operator (Lasso) and the double-selection Lasso, this study provides novel evidence that Body Mass Index (BMI) is a single risk factor with long-term predictability for CVD odds ratio, selected apart from age, which is non-modifiable. The lasting effect of BMI on CVD risk remains robust and consistent across different methods and specifications that account for variable selection errors in high-dimensional logit regression and BMI’s time trends. In addition to the long-term predictive role of BMI in CVD risk, the disease burden associated with increased BMI is quantified by comparing the marginal effects of BMI to those of age across various groups. The marginal effect of elevated BMI is more pronounced in men than women and among the employed compared to the non-employed. Leading a healthy lifestyle through the control of BMI is a critical element for preventing CVD based on the empirical findings of the current study.