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Building and Validating an Explainable Machine Learning Model for Predicting Health-Promoting Behaviors in Older

Pingping Zhang1, Yutong Hou1, Yidan Zhai2

  • 1Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.

Prevention Science : the Official Journal of the Society for Prevention Research
|March 24, 2026
PubMed
Summary
This summary is machine-generated.

A new machine learning model accurately predicts health-promoting behaviors in older adults. Key predictors include internet skills and functional independence, aiding personalized health interventions.

Keywords:
AgedHealth-promoting behaviorsMachine learningPrediction modelSHAP

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Area of Science:

  • Gerontology
  • Health Informatics
  • Machine Learning

Background:

  • Enhancing health-promoting behaviors (HPBs) is vital for chronic disease management and healthy aging in an aging population.
  • Accurate assessment of individual HPB levels is essential for developing personalized interventions.
  • Existing methods for assessing HPBs in older adults may lack precision and personalization.

Purpose of the Study:

  • To identify factors influencing HPBs in older adults using multicenter data.
  • To develop and validate an interpretable machine learning (ML) model for predicting HPBs in this demographic.
  • To create a user-friendly tool for healthcare professionals to assess HPB levels.

Main Methods:

  • A multicenter cross-sectional study involving 781 older adults across Shanghai, Jiangsu, and Shandong.
  • Data collection included sociodemographic characteristics, health status, community sports facility conditions, mobile phone proficiency, and internet skills.
  • A Stochastic Gradient Boosting Trees (SGBT) model was developed and validated using independent external test sets, with performance evaluated by AUC, accuracy, specificity, PPV, NPV, recall, F1-score, calibration tests, and decision curve analysis (DCA).

Main Results:

  • The SGBT model demonstrated high performance on the external test set with an AUC of 0.891, accuracy of 0.895, and excellent calibration (Brier score = 0.103).
  • Key predictors identified by SHAP analysis included internet proficiency, educational level, and functional independence.
  • Decision curve analysis indicated significant clinical utility across a wide probability threshold range.

Conclusions:

  • A high-performance, interpretable ML model was successfully developed to predict health-promoting behaviors in older adults.
  • The model and identified predictors can assist healthcare professionals in rapidly assessing HPB levels.
  • This tool facilitates the precise delivery of tailored health information and services for improved aging outcomes.