Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients

doi:10.4251/wjgo.121356

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World Journal of Gastrointestinal Oncology

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1948-5204

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Oncol. Sep 15, 2026; 18(9): 121356
Published online Sep 15, 2026. doi: 10.4251/wjgo.121356

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Figure 3 Receiver operating characteristic curves of six machine learning models on the validation set. ROC: Receiver operating characteristic; AUC: Area under the curve; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; SVM: Support vector machine; ANN: Artificial neural network.

Citation: Luo Q, Zhang C, Luo YP. Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients. World J Gastrointest Oncol 2026; 18(9): 121356
URL: https://www.wjgnet.com/1948-5204/full/v18/i9/121356.htm
DOI: https://dx.doi.org/10.4251/wjgo.121356

Luo Q, Zhang C, Luo YP. Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients. World J Gastrointest Oncol 2026; 18(9): 121356 [DOI: 10.4251/wjgo.121356]

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