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

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 fore...

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Bibliographic Details
Published in:Journal of clinical laboratory analysis 2024-09, Vol.38 (17-18), p.e25095-n/a
Main Authors: Yousefabadi, Sahar Arab, Ghiasi Hafezi, Somayeh, Kooshki, Alireza, Hosseini, Marzieh, Mansoori, Amin, Ghamsary, Mark, Esmaily, Habibollah, Ghayour‐Mobarhan, Majid
Format: Article
Language:English
Subjects:
Abdomen
Adipose tissue
anthropometric indices
Atherosclerosis
Body mass index
Cardiovascular disease
Cholesterol
Decision trees
Disease prevention
Dyslipidemia
Females
High density lipoprotein
machine learning methods
Males
Metabolic disorders
Obesity
Overweight
Prediction models
predictor
Risk assessment
Risk factors
Software
total cholesterol
Variables
Womens health
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Summary: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
ISSN:0887-8013
1098-2825
1098-2825
DOI:10.1002/jcla.25095