From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in adolescents.

ABSTRACT:

Background

Non-alcoholic fatty liver disease (NAFLD) is increasingly prevalent among adolescents and poses a significant public health challenge. Due to limitations in imaging and invasive diagnostic methods such as liver biopsy, there is a pressing need for accurate, cost-effective, and non-invasive risk prediction tools. This study aims to develop and compare multiple machine learning (ML) models to predict NAFLD risk in adolescents using routine anthropometric and laboratory data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 dataset.

Methods

Data from 2,132 U.S. adolescents (NHANES 2011-2020) were analyzed. Nine machine learning (ML) models were developed using features selected by Light Gradient Boosting Machine (LightGBM). Performance was

SUBMITTER: Zhang C 

PROVIDER: S-EPMC12605207 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature