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Updated: May 8, 2026

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and

Natthanaphop Isaradech^1,2, Wachiranun Sirikul^1,3,4, Nida Buawangpong⁵

¹Department of Community Medicine, Faculty of Medicine, Chiang Mai University, 110, Intrawarorot Road, Meaung, 50200, Thailand, 66 53935472, 66 935476.

|April 22, 2025

Summary

Machine learning models can now screen for frailty in Thai older adults. This approach uses simple data to identify individuals needing early intervention for better health outcomes.

Keywords:

AI Thailand aged care aging artificial intelligence clinical decision support community dwelling delivering health information and knowledge to the public diagnosis diagnostic systems digital health epidemiology frailty geriatric gerontology health care intervention machine learning old patient care prediction predictive surveillance

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Area of Science:

Gerontology
Artificial Intelligence
Public Health

Background:

Frailty is a state of increased vulnerability in older adults, leading to higher morbidity and mortality.
Early identification and intervention can improve physical function and health outcomes in frail individuals.
There's a lack of machine learning integration for frailty screening in Thailand, despite global evidence.

Purpose of the Study:

To develop and validate a machine learning (ML) model for early frailty diagnosis in community-dwelling older adults in Thailand.
To utilize individual characteristics and anthropometric data for ML model generation.
To address the gap in ML-based frailty surveillance in Thailand.

Main Methods:

Utilized datasets of 2692 community-dwelling Thai older adults from Lampang (2016-2017) for model development and internal validation.
Externally validated models using a dataset from Chiang Mai (2021).
Implemented and compared various ML algorithms: k-nearest neighbors, random forest, multilayer perceptron, logistic regression, gradient boosting, and support vector machine.

Main Results:

Logistic regression demonstrated the best performance, with an area under the receiver operating characteristic curve of 0.81 (internal validation) and 0.75 (external validation).
The developed model was well-calibrated to the expected probability in the external validation dataset.
Multiple ML algorithms were evaluated for their effectiveness in frailty detection.

Conclusions:

The developed ML models show potential as a screening tool for frailty in Thai older adults.
The models use simple, accessible demographic and clinical variables for early identification.
This approach can facilitate early intervention to help individuals regain physical robustness.