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CORRECTION article

Front. Endocrinol., 05 February 2026

Sec. Translational and Clinical Endocrinology

Volume 17 - 2026 | https://doi.org/10.3389/fendo.2026.1763989

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

This article is a correction to:

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

    1. Read original article
Chenming Zhang&#x;Chenming Zhang1†Bin Niu,&#x;Bin Niu2,3†Rong Wang,&#x;Rong Wang2,3†Liaoyun Zhang*Liaoyun Zhang2*
  • 1Academy of Medical Sciences, Shanxi Medical University, Taiyuan, China
  • 2Department of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, China
  • 3Graduate School, Shanxi Medical University, Taiyuan, China

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

By Zhang C, Niu B, Wang R and Zhang L (2025). Front. Endocrinol. 16:1681686. doi: 10.3389/fendo.2025.1681686

There was a mistake in Tables 2–4 as published. Tables 2–4 contained a formatting/layout issue: in each table, an extra blank table appeared above the intended populated table.

Table 2
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Table 2. Performance comparison of nine machine learning models for NAFLD prediction in the testing set.

Table 3
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Table 3. Performance comparison between the ET model and TYG-based indicators in the testing set.

Table 4
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Table 4. Performance comparison of the ET model and logistic regression models based on TYG and its derived indices in the testing set.

The corrected Tables 2–4 appear below.

The original version of this article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Reviewed by:

Bikash Sadhukhan, Techno International New Town, India
Maria Teofila Vicente Herrero, University of Balearic Islands, Spain

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, emx5c2d6eUAxNjMuY29t

†These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.