Machine learning to identify precachexia and cachexia: a multicenter, retrospective cohort study.

ABSTRACT:

Background

Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.

Objective

This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia.

Methods

We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia popu

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11371878 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature