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Related Experiment Video

Updated: May 31, 2025

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Predicting Hospitalization in Older Adults Using Machine Learning.

Raymundo Buenrostro-Mariscal1, Osval A Montesinos-López1, Cesar Gonzalez-Gonzalez1

  • 1School of Telematics, University of Colima, Colima 28040, Mexico.

Geriatrics (Basel, Switzerland)
|January 23, 2025
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Summary
This summary is machine-generated.

Predicting hospitalizations in older adults is crucial. A random forest model identified functional limitations, age, and cerebrovascular accidents as key predictors, aiding healthcare resource allocation.

Keywords:
health predictionhospitalizationmachine learningolder adultsrandom forest

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Area of Science:

  • Gerontology
  • Public Health
  • Data Science

Background:

  • Hospitalization rates among older adults in Mexico are increasing, driven by chronic diseases and strained healthcare resources.
  • The Mexican Health and Aging Study (MHAS) provides longitudinal data crucial for understanding hospitalization trends.

Purpose of the Study:

  • To develop a predictive model for hospitalization in older adults using the random forest (RF) algorithm.
  • To identify key predictors of hospitalization by analyzing variable importance.

Main Methods:

  • An RF model was developed and evaluated using various data partitioning strategies and variable interaction.
  • Modified nested cross-validation ensured model robustness, with sensitivity, specificity, and kappa coefficient as evaluation metrics.
  • Variable importance was assessed using mean decrease in impurity and permutation importance.

Main Results:

  • The optimal model (ST2 with interaction and 20% test proportion) achieved a sensitivity of 0.7215 and specificity of 0.4935.
  • Key predictors included functional limitations (31.1%), age (12.75%), cerebrovascular accident history (12.4%), and education level (12.08%).
  • The model effectively captured complex interactions between health and socioeconomic factors.

Conclusions:

  • Variable importance analysis enhances RF model interpretability for predicting older adult hospitalizations.
  • Findings offer insights for clinical applications, including hospital demand forecasting and resource optimization.
  • Future research will explore subgroup analyses for comorbidities and advanced missing data techniques.