Development of machine learning-based models for predicting sarcopenia risk in stroke patients and analysis of associated factors

Jianxiang Wang; Maorong Mu; Jiayue Zhang; Xiaojiao Yin; Yun Gao

doi:10.1038/s41598-025-31600-2

Development of machine learning-based models for predicting sarcopenia risk in stroke patients and analysis of associated factors

J Jianxiang Wang M Maorong Mu J Jiayue Zhang X Xiaojiao Yin Y Yun Gao (Engineering Research Center of Advanced Rare Earth Materials, Department of Chemistry)

Download PDF View Original

APA MLA Chicago BibTeX RIS

Article Details

Journal Scientific Reports

Volume / Issue Vol. 16, Issue 1

Published December 08, 2025

DOI 10.1038/s41598-025-31600-2

ISSN 2045-2322

Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (5)

J

Jianxiang Wang

M

Maorong Mu

J

Jiayue Zhang

X

Xiaojiao Yin

Y

Yun Gao

Engineering Research Center of Advanced Rare Earth Materials, Department of Chemistry

Development of machine learning-based models for predicting sarcopenia risk in stroke patients and analysis of associated factors

Article Details

Journal Info

Authors (5)

Related Articles from this Journal

AI models of unstable flow exhibit hallucination

The MCAA-YOLO + XPBI integrated model provides a hybrid intelligent measurement method for predicting the body weight of Hu sheep

Application of iron nano-biofertilizers as a sustainable approach to enhance growth and micronutrient uptake in soybean

Adolescents’ status-dependent public and private conformity to prosocial and antisocial decisions in a public goods game

An explainable hierarchical capsule network framework for supporting expert sensory evaluation of herbal medicines with natural language generation