Table 10.

Performance scores of individual algorithms and ensemble model

	GB	QDA	ET	DT	RF	LR	ANN	ELM
Specificity	0.6276	0.5586	0.7724	0.8276	0.8621	0.8345	0.5931	0.9034
Sensitivity	0.9190	0.9467	0.7719	0.4670	0.7868	0.8102	0.9446	0.7655
Precision	0.8887	0.8740	0.9165	0.8975	0.9486	0.9406	0.8825	0.9625
CV—Specificity	0.6099	0.5768	0.7841	0.8445	0.8425	0.8362	0.5768	0.8861

Gradient Boosting (GB), Quadratic Discriminant Analysis (QDA), Extra Trees (ET), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), Ensemble Learning Model (ELM)