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Development and validation of a machine learning model to predict stroke risk based on the NHANES database.

Junzhang Huang1, Wencai Liu2

  • 1Department of General Surgery, Lianjiang Traditional Chinese Medicine Hospital, Zhanjiang, Guangdong, China.

Medicine
|November 8, 2025
PubMed
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Machine learning models accurately predict stroke risk using National Health and Nutrition Examination Survey data. Different variable selection methods impact predictive accuracy, highlighting the potential for clinical application.

Area of Science:

  • Medical informatics
  • Biostatistics
  • Public health

Background:

  • Stroke poses a significant global health burden with high incidence and mortality rates.
  • Accurate stroke risk assessment is crucial for effective prevention and clinical management.
  • Machine learning offers promising tools for enhancing predictive capabilities in healthcare.

Purpose of the Study:

  • To evaluate the efficacy of machine learning models for stroke risk prediction.
  • To compare different variable selection techniques in developing predictive models.
  • To identify key predictors of stroke using robust analytical methods.

Main Methods:

  • Utilized data from the National Health and Nutrition Examination Survey (1999-2002).
  • Applied LASSO regression with stepwise selection, random forest, and Boruta algorithm with LASSO regression for variable selection and model construction.
Keywords:
LASSO regressionboruta algorithmmachine learningrandom foreststroke

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  • Assessed model performance using receiver operating characteristic (ROC) curves, precision-recall curves, calibration, and decision curve analyses.

    • Main Results:

    • A LASSO and stepwise regression model demonstrated strong discriminative performance with an Area Under the Curve (AUC) of 0.843.
    • The Boruta algorithm followed by LASSO regression achieved a comparable AUC of 0.828.
    • Random forest yielded lower predictive accuracy (AUC = 0.612) with fewer selected predictors.

    Conclusions:

    • Different variable selection methods significantly influence the predictive accuracy of stroke risk models.
    • Machine learning models, particularly those employing LASSO and Boruta algorithms, show high accuracy and clinical value for stroke risk prediction.
    • The developed models utilizing the NHANES database provide a reliable tool for predicting stroke risk, aiding in prevention and intervention strategies.