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Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features

Crossref DOI link: https://doi.org/10.1007/s40520-025-02975-z

Published Online: 2025-03-01

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Lian, Rongna

Tang, Huiyu

Chen, Zecong

Chen, Xiaoyan

Luo, Shuyue

Jiang, Wenhua

Jiang, Jiaojiao

Yang, Ming
Funding

Funding for this research was provided by:

Health and Family Planning Commission of Sichuan Province (No. ZH2023-103)
License Information

Text and Data Mining valid from 2025-03-01

Version of Record valid from 2025-03-01
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Article History

Received: 28 November 2024

Accepted: 14 February 2025

First Online: 1 March 2025

Declarations

:

: Conceptualization, MY and JJ; data curation, ZC, XC, SL, and WJ; investigation, LR, ZC, and XC; methodology, HT and RL; formal analysis, HT and RL; funding acquisition, MY; writing - original draft, HT and RL; writing - review and editing, RL, HT, JJ, and MY.

: No Generative AI or AI-assisted technologies were used in the writing process.

: The authors declare no competing interests.

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