Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.

ABSTRACT:

Objectives

This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.

Methods

This study used data from 3,549 participants aged 60 and older in the China Health and Retirement Longitudinal Study (CHARLS). Sarcopenia status was evaluated by the AWGS2019 algorithm. Full models for 2- and 4-year sarcopenia risk, considering multifactorial baseline variables, were compared with modifiable models. Eight machine learning (ML) algorithms were used to build these models, with performance evaluated by the area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) was applied for model explanation.

SUBMITTER: Chen X 

PROVIDER: S-EPMC11866656 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature