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Updated: Jul 17, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?

Razieh Mirzaeian1, Raoof Nopour2, Zahra Asghari Varzaneh3

  • 1Department of Health Information Management, Shahrekord University of Medical Sciences, Shahrekord, Iran.

Biomedical Engineering Online
|August 29, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts successful aging (SA) by identifying key influencing factors. The Random Forest algorithm demonstrated superior performance in this predictive modeling, offering a valuable tool for improving elderly care.

Keywords:
AgedData miningHealth-related quality of lifeMachine learningQuality of lifeSuccessful aging

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Area of Science:

  • Gerontology and Public Health
  • Computational Health Sciences

Background:

  • Global increase in life expectancy necessitates focus on successful aging (SA) as a health quality indicator.
  • SA is a complex, multidimensional concept, making its definition and measurement challenging.
  • Identifying factors influencing SA is crucial for developing effective interventions.

Purpose of the Study:

  • To identify key factors impacting successful aging (SA) in older adults.
  • To develop and evaluate machine learning (ML) models for predicting SA.
  • To determine the most effective ML algorithm for SA prediction.

Main Methods:

  • Data collected via interviews from 1465 adults aged 60+ in Abadan, Iran (2021-2022).
  • Binary logistic regression used to identify significant factors related to SA.
  • Eight ML algorithms (including Random Forest, XGBoost, SVM) were trained and evaluated for SA prediction accuracy.

Main Results:

  • 44 factors were found to have a significant relationship with successful aging (SA).
  • The Random Forest (RF) algorithm achieved the highest performance in predicting SA.
  • RF model achieved 0.94 accuracy, 0.95 sensitivity, 0.94 specificity, and 0.94 F-score.

Conclusions:

  • The Random Forest algorithm significantly outperformed other ML methods in predicting successful aging.
  • Developed ML models offer a reliable tool for gerontologists, healthcare providers, and policymakers.
  • These models can aid in improving health outcomes for the elderly population.