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



Yousefabadi S1 ; Ghiasihafezi S2 ; Kooshki A1 ; Hosseini M3 ; Mansoori A2 ; Ghamsary M4 ; Esmaily H2, 5 ; Ghayourmobarhan M6
Authors
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Authors Affiliations
  1. 1. Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
  2. 2. Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
  3. 3. Department of Biostatistics, College of Health, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. School of Public Health, Loma Linda University, Loma Linda, CA, United States
  5. 5. Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
  6. 6. Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Source: Journal of Clinical Laboratory Analysis Published:2024


Abstract

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. © 2024 The Author(s). Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.
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