Table 5.

Predictive performance of models for PMO.

	ML regression	glmnet	svm
No-information-rate	$0.67$	$0.67$	$0.67$
Number of parameters	3	8	30
Correct classification rate	$0.82$	$0.84$	$0.83$
(95% confidence interval)	(0.75–0.88)	(0.77–0.90)	(0.76–0.89)
Kappa agreement rate	$0.58$	$0.60$	$0.60$
Area under curve (AUC)	$0.83$	$0.85$	$0.85$
Sensitivity	$0.92$	$0.97$	$0.93$
Specificity	$0.63$	$0.58$	$0.63$
Pos. predictive value (PPV)	$0.83$	$0.82$	$0.84$
Neg. predictive value (NPV)	$0.79$	$0.89$	$0.82$
Pos. likelihood ratio (LR+)	$2.5$	$2.3$	$2.5$
Neg. likelihood ratio (LR−)	$0.13$	$0.05$	$0.11$