Surgery cancellations waste scarce operative resources and hinder patients' access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models - random forest, support vector machine, and XGBoost - were used for the identification of surgeries with high risks of cancellation. The optimal performances of the identification models were as follows: sensitivity - 0.615; specificity - 0.957; positive predictive value - 0.454; negative predictive value - 0.904; accuracy - 0.647; and area under the receiver operating characteristic curve - 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. This study is a new application of machine learning for the identification of surgeries with high risks of cancellation and facilitation of surgery resource management.
Keywords: elective surgery; hospital information system; identification; machine learning; surgery cancellation.