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Related Experiment Video

Updated: May 23, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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A TyG-UHR-based machine learning model for screening lean MAFLD: development and external validation.

Yansong Ji1, Hui Zhang2, Jingjing Xia3

  • 1Health Management Center, the Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University, Huai'an, 223300, China. jiyansong1988@gmail.com.

Biomedical Engineering Online
|May 12, 2026
PubMed
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A new machine learning model uses routine health data to screen for lean metabolic dysfunction-associated fatty liver disease (MAFLD). This cost-effective tool aids primary care triage and can be adapted for different populations.

Area of Science:

  • Hepatology
  • Machine Learning
  • Public Health

Background:

  • Lean metabolic dysfunction-associated fatty liver disease (MAFLD) is underdiagnosed in primary care due to low risk perception and imaging limitations.
  • Cost-effective screening using routine data is needed to improve early detection and referral for MAFLD.

Purpose of the Study:

  • To develop and validate an explainable machine learning model for screening lean MAFLD using routinely collected health checkup data.
  • To assess the model's performance, calibration, and clinical utility in both internal and external validation cohorts.

Main Methods:

  • Developed an Extreme Gradient Boosting (XGBoost) model using triglyceride-glucose (TyG) and uric acid-to-high-density lipoprotein cholesterol ratio (UHR), plus other clinical predictors.
  • Utilized elastic-net regression for predictor selection and rigorous leakage control during model tuning.
Keywords:
CalibrationDecision curve analysisExternal validationHealth checkupLean MAFLDMachine learningSHAPTyGUHR

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  • Externally validated the model on a separate cohort, employing post hoc recalibration to improve accuracy.

    • Main Results:

    • The XGBoost model demonstrated high discrimination (AUC 0.995 in test set) and improved probability accuracy after recalibration in external validation (Brier score 0.109).
    • Key predictors included TyG, systolic blood pressure, and UHR, with the model showing significant net benefit across various thresholds.
    • The model proved interpretable via SHAP values, highlighting TyG as the most influential factor.

    Conclusions:

    • An explainable machine learning model using TyG and UHR can effectively and affordably screen for lean MAFLD from routine data.
    • The model supports primary care triage and can be adapted to external populations via recalibration for easier implementation.
    • Paired prediction with local recalibration is recommended to maintain accuracy across diverse healthcare settings.