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Related Experiment Video

Updated: Jul 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Development and Internal Validation of a Machine Learning-Based Classification Model for Identifying Cognitive

Sijing Wang1, Tingting Tan2, Qingqing Wang2

  • 1Nursing Department, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, People's Republic of China.

Clinical Interventions in Aging
|July 6, 2026
PubMed
Summary

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Cognitive frailty (CF) is common in older adults with type 2 diabetes mellitus (T2DM). A simple model using eight factors can help identify CF early for better management.

Area of Science:

  • Gerontology
  • Clinical Medicine
  • Data Science

Background:

  • Cognitive frailty (CF) is a significant concern in older adults with type 2 diabetes mellitus (T2DM).
  • CF increases the risk of adverse outcomes and requires early detection for potential reversibility.
  • Efficient bedside tools are needed for identifying CF in this population.

Purpose of the Study:

  • To develop and validate a parsimonious model for early identification of cognitive frailty in older adults with T2DM.
  • To assess the performance and interpretability of machine learning algorithms for CF detection.
  • To identify key indicators associated with cognitive frailty in this demographic.

Main Methods:

  • A total of 523 older adults (≥65 years) with T2DM were included.
Keywords:
cognitive frailtyhealth ecological theoryidentificationmachine learningolder adultstype 2 diabetes mellitus

Related Experiment Videos

Last Updated: Jul 7, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

  • LASSO regression selected eight key features from 35 candidate variables.
  • Six machine learning algorithms were trained and evaluated for CF identification, with SHAP used for interpretability.

    • Main Results:

    • CF prevalence was 41.5% in the study sample.
    • Key indicators identified were age, cognitive activities, physical exercise, diabetes complications, nutritional status, depressive symptoms, social support, and insomnia symptoms.
    • Logistic regression achieved the highest performance (AUC 0.947) in identifying CF, with SHAP analysis highlighting risk factors.

    Conclusions:

    • A parsimonious model with eight readily available features shows strong internal validity for identifying CF in older T2DM inpatients.
    • This tool can aid in early screening and clinical decision-making.
    • Further prospective studies are needed to confirm the clinical utility of this model.