Table 5.

Detailed performance metrics for all models in validation cohorts.

Model	Accuracy	Sensitivity	Specificity	F1
Logistic	0.83 (0.654 - 0.923)	0.774 (0.518 - 0.968)	0.909 (0.738 - 1.000)	0.842 (0.648 - 0.933)
SVM	0.83 (0.672 - 0.923)	0.871 (0.679 - 1.000)	0.773 (0.500 - 1.000)	0.857 (0.713 - 0.944)
NeuralNetwork	0.811 (0.662 - 0.912)	0.774 (0.645 - 1.000)	0.864 (0.500 - 1.000)	0.828 (0.695 - 0.926)
Xgboost	0.849 (0.654 - 0.923)	0.871 (0.518 - 0.972)	0.818 (0.750 - 1.000)	0.871 (0.678 - 0.948)
KNN	0.83 (0.711 - 0.962)	0.806 (0.603 - 1.000)	0.864 (0.750 - 1.000)	0.847 (0.674 - 0.932)
Adaboost	0.755 (0.672 - 0.923)	0.806 (0.580 - 1.000)	0.682 (0.577 - 1.000)	0.794 (0.657 - 0.913)
CatBoost	0.849 (0.769 - 0.982)	0.903 (0.720 - 1.000)	0.773 (0.625 - 1.000)	0.865 (0.711 - 0.948)