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 2 Feature selection. A: Least absolute shrinkage and selection operator coefficient path; B: Least absolute shrinkage and selection operator regularization path diagram; C: Boruta algorithm; D: Final feature subset. CEA: Carcinoembryonic antigen; TNM: Tumor-node-metastasis; CA199: Carbohydrate antigen 19-9; AFP: Alpha-fetoprotein; LDH: Lactate dehydrogenase; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HTN: Hypertension; DM: Diabetes mellitus; CHD: Coronary heart disease; RBC: Red blood cell count.

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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