BMC Medical Informatics and Decision Making

Table 3 Performance evaluation of six machine learning algorithms

From: A machine learning–based framework for predicting postpartum chronic pain: a retrospective study

	CE	F1 score	AUC	PRAUC	Precision	Recall	Sensitivity	Specificity
KNN	0.212	0.789	0.862	0.817	0.751	0.830	0.830	0.751
Logistic	0.342	0.628	0.719	0.676	0.652	0.606	0.606	0.706
LDA	0.343	0.627	0.720	0.675	0.648	0.607	0.607	0.702
Naive bayes	0.346	0.629	0.711	0.660	0.642	0.616	0.616	0.689
Ranger	0.219	0.775	0.856	0.834	0.757	0.794	0.794	0.771
xgboost	0.147	0.851	0.876	0.821	0.822	0.882	0.882	0.827

Abbreviations: Areas under the curve, AUC; Classification error, CE; K-nearest neighbor, KNN; linear discriminant analysis, LDA; Precision and recall areas under the receiver operating characteristic curve, PRAUC

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ISSN: 1472-6947

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