Factors associated with underweight, overweight, and obesity in Chinese children aged 3-14 years using ensemble learning algorithms.

ABSTRACT:

Background

Factors underlying the development of childhood underweight, overweight, and obesity are not fully understood. Traditional models have drawbacks in handling large-scale, high-dimensional, and nonlinear data. In this study, we aimed to identify factors responsible for underweight, overweight, and obesity using machine learning methods among Chinese children.

Methods

Our study participants were children aged 3-14 from 30 kindergartens and 26 schools in Beijing and Tangshan. Weight status was defined per the World Health Organization criteria. We implemented three ensemble learning algorithms and compared their performance and ranked the contributing factors by importance and identified an optimal set. A user-friendly web application was developed to calculate the pr

SUBMITTER: Chen K 

PROVIDER: S-EPMC11804908 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature