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Related Experiment Video

Updated: Jun 30, 2026

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome
06:04

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome

Published on: September 27, 2024

Developing and evaluating machine learning-based risk models for metabolic syndrome among nurses: a cross-sectional

Xiaoling Feng1, Xinqing Zhu1, Jie Peng1

  • 1Department of Nursing, The Sixth Affiliated Hospital of Guangxi Medical University, Yulin, China.

Frontiers in Public Health
|June 29, 2026
PubMed
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Nurses face high metabolic syndrome risk. A random forest model accurately predicts this condition using factors like BMI and job burnout, enabling early intervention for improved nurse health.

Area of Science:

  • Occupational Health
  • Cardiovascular Disease Risk Factors
  • Machine Learning in Healthcare

Background:

  • Metabolic syndrome, a cluster of cardiovascular disease risk factors, poses a significant threat to public health.
  • Nurses are at increased risk due to demanding work environments and irregular lifestyles.
  • Early detection of metabolic syndrome is crucial for preventing complications and enhancing nurse well-being.

Purpose of the Study:

  • To develop and validate a machine learning model for predicting metabolic syndrome in nurses.
  • To identify key risk factors contributing to metabolic syndrome among the nursing population.
  • To provide a tool for early identification and targeted interventions for high-risk nurses.

Main Methods:

  • A cross-sectional survey collected data from 900 nurses.
Keywords:
machine learningmetabolic syndromenursesoccupational healthprediction model

Related Experiment Videos

Last Updated: Jun 30, 2026

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome
06:04

Pulse-Wave Velocity, Flow-Mediated Dilation, and Carotid Intima-Media Thickness to Assess Cardiovascular Risk in Population with Metabolic Syndrome

Published on: September 27, 2024

  • Machine learning models including logistic regression, random forest, XGBoost, support vector machine, and neural networks were employed for prediction.
  • SHAP values were utilized to interpret feature importance, and external validation was performed on an additional 227 nurses.

    • Main Results:

    • The random forest model demonstrated superior predictive performance with an AUC of 0.890 on the test set and 0.80 on external validation.
    • Key predictors identified included Body Mass Index (BMI), job burnout, shift work, and age.
    • The model effectively identified 9 significant characteristics influencing metabolic syndrome prediction.

    Conclusions:

    • The study successfully developed an accurate random forest prediction model for metabolic syndrome in nurses.
    • Identified key factors like BMI and job burnout offer theoretical support for early, personalized health interventions.
    • The model serves as a quantitative tool to improve nurse health, work efficiency, and inform occupational health policies.