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 7 Dependence plots of the support vector machine model. A-D: The X-axis represents feature values, and the Y-axis represents SHapley Additive exPlanation (SHAP) values (contribution to the predicted probability). Each dot denotes an individual sample. Positive SHAP values indicate an increased risk of esophagogastric variceal bleeding, while negative values indicate a decreased risk. SHAP: SHapley Additive exPlanation.

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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