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Introduction <p>Our study applies machine learning methods to identify determinants of low–high-density lipoprotein cholesterol (HDL-C) in northeastern Iran. Clarifying these risk factors may support earlier diagnosis and treatment of cardiovascular disease (CVD) and inform timely prevention strategies.</p> Methods <p>This analytic cross-sectional study used baseline data from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort to develop predictive models of factors associated with low HDL-C. Participants were stratified into two groups based on HDL-C cut-off values: 40 mg/dL for men and 50 mg/dL for women. Our objective was to construct and evaluate predictive models to identify key factors associated with low HDL-C using Logistic Regression (LR), Decision Tree (DT), and Bootstrap Forest (BF).</p> Results <p>Among the 7526 participants assessed, 4842 (64.3%) were identified with low HDL-C levels. Logistic regression analysis demonstrated that physical activity level (PAL) was the most influential determinant, followed by sex and hip circumference. In parallel, the Bootstrap Forest model underscored mid-upper arm circumference and demi-span as the principal predictors of HDL-C status.</p> Conclusion <p>PAL, sex, hip circumference, mid-upper arm circumference, and demi-span emerged as potential predictors of HDL-C levels. Moreover, DT and BF models demonstrated robust capabilities in constructing predictive models for HDL-C-related factors.</p>

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Machine Learning-Based Predictive Modeling of Factors Associated with Low HDL-C Levels: Insights from a Large-Scale Cohort Study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月20日

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  • Amin Mansoori,
  • Bahareh Behkamal,
  • Susan Darroudi,
  • Niloofar Nateghi,
  • Sara Saffar Soflaei,
  • Bahram Shahri,
  • Hedieh Alimi,
  • Habibollah Esmaily,
  • Gordon A. Ferns,
  • Majid Ghayour-Mobarhan,
  • Mohsen Moohebati,
  • Mohammad Reza Saberi

摘要

Introduction

Our study applies machine learning methods to identify determinants of low–high-density lipoprotein cholesterol (HDL-C) in northeastern Iran. Clarifying these risk factors may support earlier diagnosis and treatment of cardiovascular disease (CVD) and inform timely prevention strategies.

Methods

This analytic cross-sectional study used baseline data from the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) cohort to develop predictive models of factors associated with low HDL-C. Participants were stratified into two groups based on HDL-C cut-off values: 40 mg/dL for men and 50 mg/dL for women. Our objective was to construct and evaluate predictive models to identify key factors associated with low HDL-C using Logistic Regression (LR), Decision Tree (DT), and Bootstrap Forest (BF).

Results

Among the 7526 participants assessed, 4842 (64.3%) were identified with low HDL-C levels. Logistic regression analysis demonstrated that physical activity level (PAL) was the most influential determinant, followed by sex and hip circumference. In parallel, the Bootstrap Forest model underscored mid-upper arm circumference and demi-span as the principal predictors of HDL-C status.

Conclusion

PAL, sex, hip circumference, mid-upper arm circumference, and demi-span emerged as potential predictors of HDL-C levels. Moreover, DT and BF models demonstrated robust capabilities in constructing predictive models for HDL-C-related factors.