What question did this study set out to answer?

This research aims to compare various machine learning models for predicting non-alcoholic fatty liver disease (NAFLD) risk in adolescents.

February 8, 2026Open Access

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

Key Points

This research aims to compare various machine learning models for predicting non-alcoholic fatty liver disease (NAFLD) risk in adolescents.
Used ensemble learning techniques to analyze data.
Performed feature selection to identify significant predictors.
Compared results across different machine learning models.
Machine learning models demonstrated varying accuracy in predicting NAFLD risk.
Key features linked to NAFLD were identified, highlighting adolescents’ vulnerabilities.

Abstract

Keywords: machine learning, non-alcoholic fatty liver disease, adolescents, feature selection, public health Citation: Zhang C, Niu B, Wang R and Zhang L (2026) Correction: From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in adolescents. Front. Endocrinol. 17:1763989. doi: 10.3389/fendo.2026.1763989 Received: 09 December 2025; Accepted: 27 January 2026; Published: 05 February 2026. Edited by: Reviewed by: Copyright © 2026 Zhang, Niu, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Liaoyun Zhang, zlysgzy@163.com † These authors have contributed equally to this work

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

Key Points

Abstract

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