Evaluating the Association of Anthropometric Indices With Total Cholesterol in a Large Population Using Data Mining Algorithms.

Background: Unbalanced levels of serum total cholesterol (TC) and its subgroups are called dyslipidemia. Several anthropometric indices have been developed to provide a more accurate assessment of body shape and the health risks associated with obesity. In this study, we used the random forest model (RF), decision tree (DT), and logistic regression (LR) to predict total cholesterol based on new anthropometric indices in a sex-stratified analysis.

Method: Our sample size was 9639 people in which anthropometric parameters were measured for the participants and data regarding the demographic and laboratory data were obtained. Aiding the machine learning, DT, LR, and RF were drawn to build a measurement prediction model.

Results: Anthropometric and other related variables were compared between both TC <200 and TC ≥200 groups. In both males and females, Lipid Accumulation Product (LAP) had the greatest effect on the risk of TC increase. According to results of the RF model, LAP and Visceral Adiposity Index (VAI) were significant variables for men. VAI also had a stronger correlation with HDL-C and triglyceride. We identified specific anthropometric thresholds based on DT analysis that could be used to classify individuals at high or low risk of elevated TC levels. The RF model determined that the most important variables for both genders were VAI and LAP.

Conclusion: We tend to present a picture of the Persian population's anthropometric factors and their association with TC level and possible risk factors. Various anthropometric indices indicated different predictive power for TC levels in the Persian population.