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Application of interpretable machine learning to predict activities of daily living disability in sarcopenia: insights from the CHARLS dataset

Published Online: 2025-10-31

Authors

Song, Zixian

Pan, Tingting

Yang, Ying

Zhang, Ze
Funding

Funding for this research was provided by:

State administration of traditional chinese medicine Geriatrics High-level Subject Fund
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Text and Data Mining valid from 2025-10-31

Version of Record valid from 2025-10-31
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Article History

Received: 8 July 2025

Accepted: 25 September 2025

First Online: 31 October 2025

Declarations

:

: The data for this study was sourced from the China Health and Retirement Longitudinal Study (CHARLS), which was approved by the Biomedical Ethics Review Committee of Peking University (Approval Number: IRB00001052-11015), and all participants signed informed consent forms. Due to the fact that this study is a secondary analysis of publicly available anonymous data, no separate ethical approval is required.The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

: Not applicable.

: The authors declare no competing interests.

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