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In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models \u2013 random forest, support vector machine, and XGBoost \u2013 were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity \u2212 0.615; specificity \u2212 0.957; positive predictive value \u2212 0.454; negative predictive value \u2212 0.904; accuracy \u2212 0.647; and area under the receiver operating characteristic curve \u2212 0.682. Of the three models, the random forest model achieved the best performance. Thus, the effective identification of surgeries with high risks of cancellation is feasible with stable performance. Models and sampling methods significantly affect the performance of identification. 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