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Functional disability screening in the elderly: a machine learning approach with ELSI-Brazil data.

Dalton Breno Costa1, Carmen Moret-Tatay2, João Carlos Néto3

  • 1Pontifícia Universidade Católica Do Rio Grande Do Sul, PUCRS, Porto Alegre, Brazil.

Geroscience
|November 1, 2025
PubMed
Summary

Machine learning models effectively predict functional disability in older adults using health and sociodemographic data. Key indicators include depressive symptoms and self-rated health, enabling early risk identification for interventions.

Keywords:
Elderly healthFunctional disabilityHealth predictionMachine learning

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Area of Science:

  • Gerontology
  • Artificial Intelligence
  • Public Health

Background:

  • Functional disability in the elderly is a growing public health concern.
  • Early identification and intervention are crucial for maintaining quality of life and reducing healthcare costs.

Purpose of the Study:

  • To investigate, validate, and apply Machine Learning (ML) algorithms for predicting functional disability in elderly individuals.
  • To identify multidimensional risk indicators for functional disability.
  • To understand factors influencing functional disability screening.

Main Methods:

  • Analysis of data from 4502 participants in the ELSI-Brazil study (2015-2016).
  • Development of classification models using 49 predictor variables (sociodemographic, health, behavioral).
  • Application of SMOTE, tenfold cross-validation, Bayesian optimization, and SHAP analysis for model interpretation.

Main Results:

  • The Ridge Classifier model demonstrated robust performance (ROC-AUC: 0.785).
  • Key predictors for functional disability included depressive symptoms, concern about mobility, and self-rated health.
  • High negative predictive value (84.5%) indicates effective screening.

Conclusions:

  • ML techniques integrated with multidimensional health data offer a promising tool for early screening and intervention of functional disability in the elderly.
  • This approach can support clinical decision-making and health policies for active and healthy aging.
  • Identifying key risk factors like mental health and self-perceived health is vital for targeted interventions.