Prediction of anesthesia recovery time for gynecological surgery by machine learning models
[Objective]To construct and verify the prediction models of anesthesia recovery time during gynecological surgery.[Methods]All patients undergoing general anesthesia from September of 2022 to August of 2023 were collected retrospectively.After data pre-processing,using these vital sign vectors,9 machine learning models were constructed:logistic regression(LR)Model,support vector machine(SVM)model,multilayer perceptron(MLP)model,adaptive boosting(AdaBoost)model,gradient boosting decision tree(GBDT)model,random forest(RF)model,extreme gradient boosting(XGBoost)model,categorical boosting(CatBoost)model,and bagging ensemble algorithm.The predictive accuracy was assessed by the receiver operating characteristic(ROC)curve and the area under the curve(AUC).In order to find the best model,the importance of each feature in the model was explored.Ten-fold cross-validation was conducted in the best model in order to explore the applicability and stability of the model.[Results]Among the 495 patients,401 patients had a recovery time of less than 2 hours from general anesthesia and 94 patients had recovered from general anesthesia for 2 hours or more.Based on the performance metrics,the optimal model was the XGBoost model,which achieved an AUC value of 0.87.According to the importance of each feature in the model,the extubation time was the most valuable feature.Through the ten-fold cross-validation,XGBoost model showed good applicability and stability.[Conclusion]XGBoost model is constructed using a variety of data,which has satiafying prediction preformance and clinical value.
万方数据
维普
