Identifying risk factors for sarcopenia using machine learning: insights from multimodal data.

ABSTRACT:

Purpose

This study aims to identify key risk factors for sarcopenia using machine learning models, leveraging anthropometric, biochemical, functional, nutritional, and genetic data. By developing predictive models, the research seeks to improve early detection, enhance diagnostic accuracy, and facilitate personalized interventions for individuals at risk of sarcopenia.

Methods

We analysed multimodal data from 484 older adults. Two scenarios: Set-a (including SARC-CalF, excluding SARC-F) and Set-b (including SARC-F, excluding SARC-CalF) were applied in a three-stage modeling process with progressively reduced features and optimized predictive performance using machine learning models. Key predictors were ranked using SHAP values, and model performance was evaluated using AUC,

SUBMITTER: Urzi F 

PROVIDER: S-EPMC12528252 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature