Table 1.

Performance of recovery risk prediction models in the validation cohort

ML Model	AUC	Sensitivity	Specificity	PPV	NPV	F1	Kappa	Balanced Accuracy
Bayesian Logistic Regression	0.824	72.7%	79.0%	8.5%	99.1%	0.152	0.111	0.759
Linear Support Vector Machine	0.816	81.8%	72.1%	7.3%	99.3%	0.134	0.090	0.770
Gradient Boosted Decision Tree	0.814	81.8%	68.6%	6.5%	99.3%	0.121	0.076	0.752
Neural network	0.813	81.8%	70.4%	6.9%	99.3%	0.127	0.127	0.761
Random Forest	0.818	85.7%	69.5%	7.0%	99.5%	0.129	0.085	0.725