Intelligent predictive risk assessment and management of sarcopenia in chronic disease patients using machine learning and a web-based tool.

ABSTRACT:

Background

Individuals with chronic diseases are at higher risk of sarcopenia, and precise prediction is essential for its prevention. This study aims to develop a risk scoring model using longitudinal data to predict the probability of sarcopenia in this population over next 3-5 years, thereby enabling early warning and intervention.

Methods

Using data from a nationwide survey initiated in 2011, we selected patient data records from wave 1 (2011-2012) and follow-up data from wave 3 (2015-2016) as the study cohort. Retrospective data collection included demographic information, health conditions, and biochemical markers. After excluding records with missing values, a total of 2891 adults with chronic conditions were enrolled. Sarcopenia was assessed based on the Asian Workin

SUBMITTER: Rong K 

PROVIDER: S-EPMC12039279 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature