Table 2.

Performance of different learning algorithms for differentiating early-stage OvCa cases from BPM in the training set using 5-fold cross validation.

Model	Hyper parameters	AUC1	Log Loss	AUCpr2	Mean per class error	RMSE3
Deep learning model	Activation: MaxoutWithDropout, hidden layers: [3, 3, 3]	0.753	0.354	0.556	0.222	0.303
Deep learning model	Activation: Tanh, hidden layers: [1,1]	0.740	0.362	0.529	0.233	0.322
StackedEnsemble	Ensemble models: GLM, Deep Learning, Random Forest, Gradient Boost Method	0.713	0.387	0.484	0.205	0.332
Deep learning model	Activation: Tanh, hidden layers: [2,2]	0.711	0.377	0.519	0.237	0.325
Lasso Regression	Lambda =0.2,5 features selected	0.709	0.506	0.438	0.202	0.376
StackedEnsemble	Ensemble models (best of each family): GLM, Deep Learning, Random Forest, Gradient Boost Method	0.692	0.399	0.459	0.228	0.336
GLM	Family: binomial	0.687	0.532	0.447	0.241	0.364
Extremely Randomized Trees (XRT)	-	0.681	0.577	0.359	0.224	0.351
Distributed Random Forest (DRF)	-	0.679	0.746	0.355	0.216	0.354
Gradient Boosting Method	Number of tree: 50, Maximum depth:6	0.668	0.516	0.357	0.234	0.372

AUC: Area under the ROC Curve

AUCpr: Area under the precision recall curve

RMSE: Root-mean-square deviation