ORIGINAL RESEARCH article
Front. Public Health
Sec. Aging and Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1661298
This article is part of the Research TopicMachine Learning-Driven Insights into Cognitive Aging and Behavioral ChangesView all 4 articles
Development and Comparative Validation of Multiple Models for Cognitive Frailty in Older Adults Residing in Nursing Homes
Provisionally accepted
- Zhejiang Chinese Medical University, Hangzhou, China
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Objectives: This study aims to develop an optimal predictive model for cognitive frailty (CF) in older adults residing in nursing homes, thereby providing a scientific basis for staff to assess CF risk and implement preventive interventions. Methods: This study recruited 500 older adults from four nursing homes in Hangzhou, Zhejiang Province, between December 2024 and March 2025 as the modeling cohort. Additionally, we enrolled 112 older adults from another nursing home in Hangzhou from March to April 2025 as the external validation cohort. With 19 variables, we applied k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) algorithms to forecast CF. The predictive performance was assessed through multiple evaluation approaches, including ROC curve evaluation, calibration curve assessment, decision curve analysis, and various classification metrics such as accuracy, precision, recall, Brier score, and the F1-score (with β=1). Furthermore, shapley additive explanations (SHAP) value analysis was performed for the optimal model. Results: Among 500 older adults in nursing homes, 132 (26.4%) exhibited CF. Essential features included the activities of daily living (ADL), frequency of intellectual activities, and age, among others. Five models using different algorithms were developed. The SVM model demonstrated the best predictive performance, with an AUC of 0.932 on the test data. External validation confirmed its accuracy (AUC = 0.751). Conclusions: Machine learning models, particularly SVM, can effectively predict CF risk in older adults residing in nursing homes. Care facility staff can utilize personal information to assess older adults and identify high-risk individuals for CF at an early stage, providing crucial support for timely interventions and quality of life enhancement.
Keywords: machine learning, cognitive frailty, nursing home, Model development, Elderly
Received: 07 Jul 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Ren, Ding, Luo, Wu, Hu, Xu and Chu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ting Chu, Zhejiang Chinese Medical University, Hangzhou, China
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