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. 2015 Apr 19;16:123. doi: 10.1186/s12859-015-0554-8

Table 3.

Performances of RF and LR on the three test sets

Test Set Tool AUC Accuracy [IC95%] Sens Spec PPV NPV F-m MCC
# 1 RF .8988 .8314 [.8381-.8246] .8354 .8274 .8298 .8331 .8326 .6629
LR .8770 .8118 [.8188-.8047] .8410 .7825 .7957 .8301 .8177 .6246
# 2 RF .90 .8310 [.8377-.8242] .8370 .8250 .8282 .8340 .8325 .6621
LR .8752 .8121 [.8190-8049] .8464 .7775 .7931 .8340 .8189 .6255
# 3 RF .9035 .8344 [.8422-8262] .8406 .8280 .8311 .8377 .8358 .6687
LR .8833 .8168 [.8250-.8083] .8459 .7875 .8003 .8355 .8225 .6346

Performances of the Random Forest (RF) and Logistic Regression (LR) on the three test sets. Area under the curve (AUC), accuracy with 95% confidence interval, sensitivity (Sens), specificity (Spec), Positive Predictive Value (PPV), Negative Predictive Value (NPV), F-measure (F-m) and Matthews correlation coefficient (MCC) are reported for each method.