Predicting Risk Factors for Dyslipidemia Based on Health Behaviors by Age in Adults Using Machine Learning

According to the 2022 Korean Society of Lipidology and Atherosclerosis, in Korea, dyslipidemia is a common disease that occurs in 40.2% of adults aged 20 or older, and its prevalence increases with age. Although dyslipidemia has a high prevalence of 47.8% in adults aged 30 or older, it is known to be preventable and manageable through lifestyle improvements in areas including eating habits, alcohol consumption, smoking, and physical activity. In this study, we propose a model for predicting age-specific dyslipidemia risk factors according to adult health behavior characteristics and diet. By analyzing the correlation between age-specific health behaviors and diet and the presence or absence of dyslipidemia, we aimed to predict dyslipidemia risk factors through a combination of multiple factor variables. This study utilized data from the 8th National Health and Nutrition Examination Survey, and selected 12,028 adults who received a doctor’s diagnosis of dyslipidemia as the subjects. In order to compare the characteristics of the dyslipidemia diagnosis group and the non-diagnosed group, a Rao–Scott χ2 test was performed, and machine learning-based logistic regression and decision tree analyses were performed to predict the dyslipidemia risk factors. Analyzing the difference in the dyslipidemia prevalence according to the general characteristics and health status showed no significant difference between the men and women in the 19–34, 35–49, and 50–64 age groups, but there was a significant difference in the dyslipidemia prevalence in the 65 and older group. It was found that the dyslipidemia risk also increased with age. In terms of health behavior characteristics, the alcohol intake frequency and aerobic exercise frequency were found to have statistically significant effects and, in terms of eating habits, the breakfast frequency and dining out frequency were found to be significant factor variables in the dyslipidemia prevalence. As a result of the decision tree analysis, the most important dyslipidemia predictive factor showed differences according to the age group. The most important predictive variable for the presence or absence of dyslipidemia in the 19–34 age group was the BMI; for the 35–49 age group, it was gender and subjective health perception; for the 50–64 age group, it was subjective health perception and the BMI; and for the 65 and older group, it was the BMI. This suggests that healthy eating habits and behaviors such as aerobic exercise are very important for preventing and managing dyslipidemia as age increases.