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

Journal: BMC geriatrics

PMID: 40016704

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.

Authors

Xiaodong Chen
Liping Li

School of Public Health, Key Laboratory of Environment and Human Health of Hebei Medical University Shijiazhuang 050017 China xuxd@hebmu.edu.cn.

Keywords

Aged China Female Humans Longitudinal Studies Machine Learning Male Middle Aged Predictive Value of Tests Risk Assessment Risk Factors Sarcopenia Time Factors

External Resources

View on PubMed Access via DOI PubMed (40016704)

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

Abstract

Authors

Keywords

External Resources

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