Key result
Machine learning models, particularly random forest, identified surgeries with high risks of cancellation with an area under the receiver operating characteristic curve of 0.682.
Why the study?
Surgery cancellations waste scarce operative resources and hinder patients' access to operative services.
Can machine learning models effectively identify surgeries with high risks of cancellation?
Can machine learning models effectively identify surgeries with high risks of cancellation?
Machine learning models, particularly random forest, can effectively identify surgeries with high risks of cancellation, facilitating better surgery resource management.
Modest accuracy (AUC 0.682) advises against routine use; leaves open whether machine learning improves surgical scheduling.
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.
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Luo et al. (2018) studied Surgery cancellation. Machine learning models (random forest, support vector machine, XGBoost) was evaluated on Identification of surgeries with high risks of cancellation (AUC 0.682). Machine learning models, particularly random forest, identified surgeries with high risks of cancellation with an area under the receiver operating characteristic curve of 0.682.
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