Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study

doi:10.3748/wjg.v31.i19.105283

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May 21, 2025 (publication date) through May 27, 2025

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World Journal of Gastroenterology

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1007-9327

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

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World J Gastroenterol. May 21, 2025; 31(19): 105283
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.105283

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Figure 6 Interpretation of the eXtreme Gradient Boosting model using SHapley Additive exPlanations. A: Mean importance ranking of features displayed by SHapley Additive exPlanations (SHAP); B: Bee colony diagram of characterization attributes in SHAP. SHAP: SHapley Additive exPlanations.

Citation: Kang BY, Qiao YH, Zhu J, Hu BL, Zhang ZC, Li JP, Pei YJ. Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study. World J Gastroenterol 2025; 31(19): 105283
URL: https://www.wjgnet.com/1007-9327/full/v31/i19/105283.htm
DOI: https://dx.doi.org/10.3748/wjg.v31.i19.105283