Prediction of Anesthesia Resuscitation after Painless Gastroenteroscopy Based on Machine Learning

Objective To construct a prediction model for post-anesthesia recovery after endoscopy and validate it. Methods Selection of 2400 surgical patients who underwent painless gastroenteroscopy at Sichuan Electric Power Hospital from September 2019 to September 2024. After data preprocessing, the Logistic regression, random forest (RF), support vector machine (SVM), decision tree (DT), and extreme gradient boosting (XGBoost) model methods were used to construct prediction models. The predictive accuracy of the machine learning models was evaluated by calculating the area under the receiver operating characteristic (ROC) curve, and the contribution of each feature to the prediction result was evaluated by using the Shapley additive explanations (SHAP) method. Results According to the performance indicators, the best model among the five models was the XGBoost regression model, and the nine indicators were found to be closely related to the recovery time of general anesthesia during the feature importance analysis. Conclusion The XGBoost model constructed using multiple data can analyze the complex relationships between variables and outcomes and has good predictive performance and clinical value.