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©The Author(s) 2025.
World J Gastroenterol. May 21, 2025; 31(19): 105283
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.105283
Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.105283
Figure 3 Testing and evaluation of eXtreme Gradient Boosting model based on raw data.
A: Area under the receiver operating characteristic curve comparison between models; B: The confusion matrix of eXtreme Gradient Boosting model; C: Comparison of decision curve analysis curves between models; D: Comparison of calibration curves between models. AUC: Area under the receiver operating characteristic curve; ROC: Receiver operating characteristic; SVM: Support vector machine; GBM: Gradient boosting machines; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine.
- 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