Front. Endocrinol. Frontiers in Endocrinology Front. Endocrinol. 1664-2392 Frontiers Media S.A. 10.3389/fendo.2026.1763989 Correction Correction: From traditional metabolic markers to ensemble learning: comparative application of machine learning models for predicting NAFLD risk in adolescents ZhangChenming 1 † Conceptualization Formal analysis Writing – original draft Software Visualization NiuBin 2 3 † Data curation Methodology Writing – review & editing WangRong 2 3 † Writing – review & editing Software Visualization Validation ZhangLiaoyun 2 * Funding acquisition Project administration Writing – review & editing Supervision Academy of Medical Sciences, Shanxi Medical University, Taiyuan, China Department of Infectious Diseases, The First Hospital of Shanxi Medical University, Taiyuan, China Graduate School, Shanxi Medical University, Taiyuan, China Correspondence: Liaoyun Zhang, zlysgzy@163.com

These authors have contributed equally to this work

 05 02 2026 2026 17 1763989 09 12 2025 27 01 2026 Copyright © 2026 Zhang, Niu, Wang and Zhang. 2026 Zhang, Niu, Wang and Zhang https://creativecommons.org/licenses/by/4.0/ 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. 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 machine learning non-alcoholic fatty liver disease adolescents feature selection public health section-at-acceptance Translational and Clinical Endocrinology

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.

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

Model AUC ACC SEN PRE F1 Score Kappa
ANN 0.715 0.778 0.470 0.285 0.355 0.230
DT 0.671 0.673 0.602 0.221 0.324 0.165
ET 0.784 0.808 0.651 0.365 0.468 0.361
GB 0.762 0.716 0.699 0.270 0.389 0.249
KNN 0.740 0.648 0.747 0.233 0.355 0.196
LightGBM 0.739 0.759 0.627 0.297 0.403 0.276
RF 0.760 0.844 0.530 0.419 0.468 0.378
SVM 0.788 0.747 0.723 0.302 0.426 0.297
XGBOOST 0.768 0.723 0.699 0.276 0.396 0.258

Models include ANN (artificial neural network), DT (decision tree), ET (extra trees), GB (gradient boosting), KNN (k-nearest neighbors), LightGBM (Light Gradient Boosting Machine), RF (random forest), SVM (support vector machine), and XGBoost (eXtreme Gradient Boosting). Metrics: AUC, area under the curve; ACC, accuracy; SEN, sensitivity; PRE, precision.

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

Model AUC ACC SEN PRE F1 Score Kappa
TYG 0.675 0.653 0.633 0.206 0.311 0.153
TYG.BMI 0.748 0.720 0.722 0.266 0.389 0.255
TYG.WC 0.760 0.714 0.823 0.278 0.415 0.283
ET 0.784 0.773 0.687 0.324 0.440 0.320

ET, extra trees; TyG, triglyceride-glucose index; BMI, body mass index; WC, waist circumference. Metrics as in Table 2.

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

Model AUC ACC SEN PRE F1 Score Kappa
TYG 0.697 0.653 0.633 0.206 0.311 0.153
TYG+TYG.BMI 0.758 0.736 0.684 0.273 0.390 0.259
TYG+TYG.WC 0.767 0.791 0.658 0.327 0.437 0.326
TYG+TYG.BMI+TYG.WC 0.768 0.797 0.696 0.342 0.458 0.351
TYG.BMI+TYG.WC 0.767 0.786 0.734 0.333 0.458 0.348
ET 0.784 0.773 0.687 0.324 0.440 0.320

ET, extra trees; TyG, triglyceride-glucose index; BMI, body mass index; WC, waist circumference. Metrics as in Table 2.

The corrected Tables 2–4 appear below.

The original version of this article has been updated.

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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