TY  - JOUR
AU  - Isaradech, Natthanaphop
AU  - Sirikul, Wachiranun
AU  - Buawangpong, Nida
AU  - Siviroj, Penprapa
AU  - Kitro, Amornphat
PY  - 2025
DA  - 2025/4/2
TI  - Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study
JO  - JMIR Aging
SP  - e62942
VL  - 8
KW  - aged care
KW  - gerontology
KW  - geriatric
KW  - old
KW  - aging
KW  - clinical decision support
KW  - delivering health information and knowledge to the public
KW  - diagnostic systems
KW  - digital health
KW  - epidemiology
KW  - surveillance
KW  - diagnosis
KW  - frailty
KW  - machine learning
KW  - prediction
KW  - predictive
KW  - AI
KW  - artificial intelligence
KW  - Thailand
KW  - community dwelling
KW  - health care intervention
KW  - patient care
AB  - Background: Frailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual’s physical function resulting in increased morbidity and mortality when exposed to acute stressors. Early identification and management can reverse individuals with frailty to being robust once more. However, we found no integration of machine learning (ML) tools and frailty screening and surveillance studies in Thailand despite the abundance of evidence of frailty assessment using ML globally and in Asia. Objective: We propose an approach for early diagnosis of frailty in community-dwelling older individuals in Thailand using an ML model generated from individual characteristics and anthropometric data. Methods: Datasets including 2692 community-dwelling Thai older adults in Lampang from 2016 and 2017 were used for model development and internal validation. The derived models were externally validated with a dataset of community-dwelling older adults in Chiang Mai from 2021. The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier. Results: Logistic regression showed the best overall discrimination performance with a mean area under the receiver operating characteristic curve of 0.81 (95% CI 0.75‐0.86) in the internal validation dataset and 0.75 (95% CI 0.71‐0.78) in the external validation dataset. The model was also well-calibrated to the expected probability of the external validation dataset. Conclusions: Our findings showed that our models have the potential to be utilized as a screening tool using simple, accessible demographic and explainable clinical variables in Thai community-dwelling older persons to identify individuals with frailty who require early intervention to become physically robust. 
SN  - 2561-7605
UR  - https://aging.jmir.org/2025/1/e62942
UR  - https://doi.org/10.2196/62942
DO  - 10.2196/62942
ID  - info:doi/10.2196/62942
ER  -