Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning.

ABSTRACT:

Background

Metabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.

Methods

A cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to

SUBMITTER: Deng X 

PROVIDER: S-EPMC10497941 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature