Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study
Abstract BackgroundFrailty 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 r...
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JMIR Publications
2025-04-01
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| Series: | JMIR Aging |
| Online Access: | https://aging.jmir.org/2025/1/e62942 |
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| author | Natthanaphop Isaradech Wachiranun Sirikul Nida Buawangpong Penprapa Siviroj Amornphat Kitro |
| author_facet | Natthanaphop Isaradech Wachiranun Sirikul Nida Buawangpong Penprapa Siviroj Amornphat Kitro |
| author_sort | Natthanaphop Isaradech |
| collection | DOAJ |
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Abstract
BackgroundFrailty 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.
ObjectiveWe 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.
MethodsDatasets 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.
ResultsLogistic 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.
ConclusionsOur 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. |
| format | Article |
| id | doaj-art-b309fbeeb1a444a590dd7b2a548b771e |
| institution | DOAJ |
| issn | 2561-7605 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Aging |
| spelling | doaj-art-b309fbeeb1a444a590dd7b2a548b771e2025-08-20T03:19:16ZengJMIR PublicationsJMIR Aging2561-76052025-04-018e62942e6294210.2196/62942Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation StudyNatthanaphop Isaradechhttp://orcid.org/0000-0001-5883-2035Wachiranun Sirikulhttp://orcid.org/0000-0002-9183-4582Nida Buawangponghttp://orcid.org/0000-0001-9735-2587Penprapa Sivirojhttp://orcid.org/0000-0003-4781-4119Amornphat Kitrohttp://orcid.org/0000-0003-4435-8090 Abstract BackgroundFrailty 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. ObjectiveWe 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. MethodsDatasets 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. ResultsLogistic 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. ConclusionsOur 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.https://aging.jmir.org/2025/1/e62942 |
| spellingShingle | Natthanaphop Isaradech Wachiranun Sirikul Nida Buawangpong Penprapa Siviroj Amornphat Kitro Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study JMIR Aging |
| title | Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study |
| title_full | Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study |
| title_fullStr | Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study |
| title_full_unstemmed | Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study |
| title_short | Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study |
| title_sort | machine learning models for frailty classification of older adults in northern thailand model development and validation study |
| url | https://aging.jmir.org/2025/1/e62942 |
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