TABLE VII.

AUROC (95% CI) When Selecting 1, 2, and 10 Best Features Using Independent Feature Selection

Classifier	AUROC (95% CI) using 1 feature	AUROC (95% CI) using 2 features	AUROC (95% CI) using 10 features
Bayesian Net	0.74 [0.70 0.77]	0.81 [0.78 0.84]	0.83 [0.80,0.86]
Lazy K Star	0.80 [0.76 0.83]	0.83 [0.80 0.86]	0.84 [0.81,0.87]
Meta Classification Regression	0.78 [0.75 0.82]	0.83 [0.80 0.86]	0.83 [0.79,0.86]
Meta Ensemble Selection	0.78 [0.74 0.81]	0.83 [0.80 0.86]	0.86 [0.83,0.89]
Alternating Decision Tree	0.78 [0.74 0.82]	0.83 [0.80 0.86]	0.83 [0.80,0.87]
Random Forest Tree	0.77 [0.74 0.81]	0.78 [0.74 0.82]	0.88 [0.85,0.91]
Classification And Regression Tree (CART)	0.76 [0.71 0.79]	0.78 [0.74 0.81]	0.82 [0.79,0.86]