Construction and evaluation of machine learning-based prediction model for live birth following fresh embryo transfer in IVF/ICSI patients with polycystic ovary syndrome

Table 3 Performance of seven machine learning-based models for predicting live birth in the testing set

Model	AUC	Accuracy	Precision	Sensitivity	Specificity	PPV	NPV	F1 score	Brier score
DT	0.773	0.679	0.669	0.619	0.738	0.669	0.694	0.643	0.194
KNN	0.719	0.643	0.594	0.581	0.705	0.594	0.694	0.587	0.258
LGBM	0.705	0.642	0.605	0.551	0.732	0.605	0.687	0.551	0.215
NBM	0.764	0.720	0.671	0.691	0.749	0.671	0.765	0.577	0.207
RF	0.794	0.702	0.669	0.64	0.765	0.669	0.741	0.654	0.184
SVM	0.806	0.266	0.202	0.243	0.29	0.202	0.34	0.221	0.461
XGB	0.822	0.752	0.682	0.772	0.732	0.682	0.812	0.724	0.172

Note: DT, decision tree; KNN, k-nearest neighbors; LGBM, light gradient boosting machine; NBM, naïve bayes model; RF, random forest; SVM, Support Vector Machine; XGB, eXtreme gradient boosting; AUC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value

ISSN: 1757-2215