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Related Experiment Video

Updated: Jul 23, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Predicting functional dependency using machine learning among a middle-aged and older Chinese population.

Qi Yu1, Zihan Li1, Chenyu Yang1

  • 1Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Archives of Gerontology and Geriatrics
|July 16, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict functional dependency in older Chinese adults. Key predictors for activities of daily living (ADL) include arthritis and age, while cognitive function and age predict instrumental activities of daily living (IADL) dependency.

Keywords:
Cohort studyEnsemble learningFunctional dependencyMachine learningPrediction model

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Area of Science:

  • Gerontology
  • Public Health
  • Artificial Intelligence in Healthcare

Background:

  • Functional dependency poses a significant challenge for aging populations worldwide.
  • Predicting the onset of functional dependency is crucial for timely interventions and healthcare planning.
  • The China Health and Retirement Longitudinal Study (CHARLS) provides a valuable dataset for investigating health trends in older Chinese adults.

Purpose of the Study:

  • To develop and validate prediction models for functional dependency in middle-aged and older Chinese adults.
  • To identify key risk factors associated with activities of daily living (ADL) and instrumental activities of daily living (IADL) dependency.

Main Methods:

  • Stacked ensemble machine learning models were constructed using data from the CHARLS cohort (≥45 years old).
  • Models were trained and tested on the 2011-2015 data and externally validated on the 2015-2018 data.
  • SHapley Additive exPlanations (SHAP) were used to determine predictor significance.

Main Results:

  • The stacked ensemble model demonstrated good predictive performance, with Area Under the Curve (AUC) values ranging from 0.690 to 0.750 in the training cohort and 0.719 to 0.727 in the validation cohort.
  • A simplified compact model retained similar predictive accuracy.
  • Significant predictors for ADL dependency included arthritis, age, self-reported health, and waist circumference.
  • Predictors for IADL dependency comprised cognitive function, age, rural living, and chair stand test performance.

Conclusions:

  • Stacked ensemble models are effective tools for identifying individuals at risk of functional dependency in the Chinese population.
  • Specific clinical and demographic factors can predict future ADL and IADL limitations, enabling targeted preventive strategies.