Predicting early postoperative PONV using multiple machine-learning- and deep-learning-algorithms

Table 3 Results of artificial intelligence algorithm forecasting early PONV in test group

Test Model name	Accuracy	Precision	Recall	F1 score	AUC
Logistic Regression	0.864	0.308	0.039	0.070	0.732
Decision Tree	0.868	0.400	0.039	0.071	0.707
SVC	0.866	0.333	0.029	0.054	0.731
gnb	0.788	0.151	0.137	0.144	0.667
knn	0.858	0.321	0.088	0.138	0.623
adab	0.865	0.400	0.078	0.131	0.722
DNN	0.861	0.294	0.049	0.084	0.694
RNN	0.814	0.203	0.147	0.170	0.611
CNNRNN	0.872	1.000	0.010	0.019	0.668

SVC Logistic Regression, Decision Tree, Linear Support Vector, gnb Gaussian naive Bayes, knn K-nearst neighbors, adab AdaBoost, DNN Artificial Neural Networks, RNN Recurrent Neural Networks, LSTM Long Short - Term Memory, CNNRNN Convolutional Neural Network + Recurrent Neural Networks

ISSN: 1471-2288