Fig. 1
Feature selection and model optimization and development. (A) Among all the clinical data collected in this study, the features marked with red stars were those associated with extubation outcomes and were utilized for subsequent modeling. On the left is the distribution plot of the model’s test and training sets, along with the proportion of extubation-success patients in each subset. (B) Flowchart of feature selection and model development. (C) Comparison of the ACC and AUC before and after optimization for each model. Accuracy (ACC), area under the receiver operating characteristic curve (AUC), K-nearest neighbors (K-NN), support vector machine (SVM), Gaussian naive Bayes (GNB), decision tree (DT), random forest (RF), extra tree classifier (ET), gradient boosting (GB), and logistic regression (LR).
