Development of a machine learning-based model for predicting postoperative survival in gastric cancer

doi:10.4240/wjgs.v18.i2.114951

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World Journal of Gastrointestinal Surgery

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1948-9366

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

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©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc.

World J Gastrointest Surg. Feb 27, 2026; 18(2): 114951
Published online Feb 27, 2026. doi: 10.4240/wjgs.v18.i2.114951

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Figure 4 SHapley Additive exPlanations analysis for the optimal extra trees model. A: Beeswarm plot summarizing feature impacts across the dataset; B: Ranking of feature importance based on mean absolute SHapley Additive exPlanations values for the extremely randomized trees model; C: Force plot illustrating the explanation for an individual prediction. TNM: Tumor-node-metastasis; CEA: Carcinoembryonic antigen; SHAP: SHapley Additive exPlanations.

Citation: Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951
URL: https://www.wjgnet.com/1948-9366/full/v18/i2/114951.htm
DOI: https://dx.doi.org/10.4240/wjgs.v18.i2.114951

Lü YN, Liu D, Tao S, Wu J, Yu SJ, Yuan HL. Development of a machine learning-based model for predicting postoperative survival in gastric cancer. World J Gastrointest Surg 2026; 18(2): 114951 [PMID: 41809349 DOI: 10.4240/wjgs.v18.i2.114951]

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