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Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Yu-Zhen Zhang1, Hai-Ying Wu2, Run-Wei Ma3

  • 1Department of Anesthesiology and Surgical Intensive Care Unit, Kunming Children's Hospital, Kunming, Yunnan, China.

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|January 25, 2025
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Summary

This study developed a simple, effective machine learning model for early detection of metabolic syndrome in adolescents using non-biochemical data. The accessible online tool aids in large-scale screening and prevention efforts.

Keywords:
AdolescentsMachine learningMetabolic syndromeNHANESPredictive model

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Area of Science:

  • Public Health
  • Biostatistics
  • Machine Learning

Background:

  • Metabolic syndrome (MetS) in adolescents is a significant public health concern, associated with obesity, hypertension, and insulin resistance.
  • Early detection and intervention are critical for preventing long-term cardiovascular and mental health issues, but current diagnostic methods can be complex.
  • Existing screening tools often rely on biochemical indicators, limiting their accessibility for large-scale, non-medical use.

Purpose of the Study:

  • To develop a predictive model for adolescent metabolic syndrome (MetS) using readily available, non-biochemical data from the NHANES database.
  • To create a simple, cost-effective tool for large-scale, non-medical screening and early prevention of MetS in adolescents.
  • To enhance accessibility and practical application through online deployment for community and school settings.

Main Methods:

  • Utilized NHANES data from 2,459 adolescents (2007-2016), excluding biochemical indicators.
  • Employed LASSO regression and 20-fold cross-validation for variable selection.
  • Built and evaluated nine predictive models, including eight machine learning models and a logistic regression model, using a 7:3 train-validation split and SMOTE for data balancing.

Main Results:

  • The LightGBM (LGB) model demonstrated superior predictive performance with an AUC of 0.969, accuracy of 0.978, and F1 score of 0.989.
  • Key predictors identified by SHAP analysis included BMI, age, sex-specific percentage, weight, upper arm circumference, thigh length, and race.
  • The validated models proved highly effective for non-medical primary screening and early warning of adolescent MetS.

Conclusions:

  • The developed LGB model offers a high-performance, accessible tool for early detection of adolescent MetS.
  • Online deployment facilitates practical application in community and school settings, supporting public health initiatives.
  • This approach promotes early intervention, potentially mitigating risks associated with metabolic syndrome in adolescents.