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This paper is in the following e-collection/theme issue:

Frailty Detection, Assessment and Prediction (80) Clinical Information and Decision Making (2167) Tools, Programs and Algorithms (430) mHealth for Screening (314) Machine Learning (2329) AI in Older Adult Care (58)

Published on in Vol 8 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62942, first published .
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Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

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

Authors of this article:

Natthanaphop Isaradech1, 2 Author Orcid Image ;   Wachiranun Sirikul1, 3, 4 Author Orcid Image ;   Nida Buawangpong5 Author Orcid Image ;   Penprapa Siviroj1 Author Orcid Image ;   Amornphat Kitro1, 2 Author Orcid Image
Article Authors Cited by (6) Tweetations Metrics

Journals

  1. Yokokawa Y, Nakamura K, Sasaki T, Yokouchi S, Kimura F. Examination of Social Participation in Older Adults Undergoing Frailty Health Checkups Using Deep Learning Models. Geriatrics 2025;10(5):124 View
  2. Aldriwesh M, Alotibi R, Alqurainy N, Alrabiah S, Arafah A, Alghoribi M, Ajina R. The role of gut microbiome in aging-associated diseases: where do we stand now and how technology will transform the future. Gut Microbes 2026;18(1) View
  3. Jung H, Kim M, Won C, Mun K. Identifying sex-specific predictors of frailty in Korean community-dwelling older adults using interpretable machine learning. BMC Geriatrics 2026;26(1) View
  4. Liao T, Gan Y, Liu L, Tang M, Gan L, Li G. Prevalence and Associated Factors of Frailty in Middle-Aged and Elderly Patients with Atrial Fibrillation: An Exploratory Machine Learning Analysis. Clinical Interventions in Aging 2026;Volume 21:1 View
  5. Kim S, Shin M, Choi B, Obradovic Z, Rubin D, Park J. Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness. Healthcare 2026;14(11):1543 View

Conference Proceedings

  1. Acosta A, Arellano K, Seblante S, Magboo M, Magboo V. 2026 4th International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA). Machine Learning Models with Explainable AI for Preoperative Frailty Risk Prediction in Elderly Patients with Hepatocellular Carcinoma View