Prediction of hemorrhage rate after tonsil surgery in children based on random forest model
Objective:Hemorrhage after tonsil surgery in children is a serious and potentially life-threatening complication.The purpose of this study was to establish a risk warning model for hemorrhage after tonsil surgery in children through a national multi-center retrospective study,providing a basis for hierarchical management after tonsil surgery in children.Methods:Stratified sampling was performed on 8 854 children who underwent tonsillec-tomy under general anesthesia from 15 research centers in different provinces from January 15,2022 to May 15,2023.The sample size of this study was 2 724 cases,including 1 096 males and 1 628 females.Children were di-vided into bleeding and non-bleeding groups according to whether or not they had bleeding after surgery.The ran-dom forest algorithm was used to build a risk warning model.By continuously exploring the optimized model,the accuracy of predicting the postoperative bleeding rate of tonsils in children was improved,and the prediction effec-tiveness of the model was verified by ten-fold cross-validation.Results:Among 2 724 children,117 had postopera-tive bleeding after tonsillectomy,with a bleeding rate of 4.30％.The model constructed by the random forest al-gorithm for the training set was verified in the test set,and the obtained prediction accuracy was 98.72％,the re-call rate was 78.95％,and the area under the ROC curve AUC was 0.96.Conclusion:Although the recall rate of the random forest model needs to be improved,the overall accuracy is quite excellent.It can effectively avoid mis-judging positive cases as negative cases.It is a useful tool that can be used to predict the postoperative bleeding rate of tonsils and clinical medical decision-making,laying a good foundation for subsequent optimization and im-provement.
NETL
NSTL
万方数据
