Machine learning system | Sensitivity | Specificity | Overall accuracy | Balanced accuracy | Variance | PPV | AUROC |
---|---|---|---|---|---|---|---|
D_FF_Conic(4x12x12x12)a | 94.1 | 88.7 | 91.4 | 92.2 | 0.5 | 93.5 | 0.90 |
D_FF_Conic(6x12x12x12)a | 92.5 | 90.2 | 91.3 | 91.7 | 0.0 | 94.1 | 0.91 |
D_FF_Bp(24) a | 89.2 | 93.0 | 91.1 | 90.6 | 1.0 | 95.6 | 0.93 |
D_FF_Bp(16x16x16x16)a | 93.2 | 88.7 | 91.0 | 91.7 | 1.0 | 93.4 | 0.92 |
D_FF_GNet(64)a | 90.7 | 90.2 | 90.5 | 90.6 | 1.0 | 94.0 | 0.90 |
D_FF_Sn(48)a | 91.7 | 88.9 | 90.3 | 90.6 | 1.0 | 93.2 | 0.92 |
D_FF_Bm(48)a | 91.6 | 88.9 | 90.2 | 90.6 | 1.0 | 93.2 | 0.91 |
D_FF_Conic(48)a | 91.6 | 88.7 | 90.2 | 90.6 | 0.0 | 93.3 | 0.92 |
D_FF_Bp(0)a | 89.1 | 90.3 | 89.7 | 89.6 | 2.1 | 93.8 | 0.91 |
MLPb | 81.0 | 84.8 | 82.9 | 82.3 | 3.1 | 89.8 | 0.90 |
RandomForestb | 86.6 | 65.3 | 75.9 | 78.7 | 1.6 | 80.6 | 0.86 |
NaiveBayesb | 85.8 | 64.5 | 75.1 | 78.1 | 4.2 | 81.0 | 0.83 |
RotationForestb | 88.3 | 60.8 | 74.6 | 78.1 | 0.0 | 79.1 | 0.85 |
Logisticb | 80.7 | 67.9 | 74.3 | 76.0 | 1.0 | 80.8 | 0.63 |
LogitBoostb | 81.0 | 61.2 | 71.1 | 73.4 | 1.6 | 77.6 | 0.81 |
J48b | 77.4 | 60.7 | 69.0 | 71. 4 | 0.5 | 77.0 | 0.57 |
SMOb | 96.8 | 27.9 | 62.4 | 70.3 | 9.9 | 61.5 | 0.65 |
kNNb | 75.9 | 36.5 | 56.2 | 60.9 | 2.6 | 66.6 | 0.56 |