Table 7
Performance measures of data mining algorithm at different levels of significance on A & C conditions
SIGNIFICANCE p < 5 x 10-4 p < 5 x 10-3 p < 5 x 10-2
Algorithm Acc. Sp Sn AUC Acc. Sp Sn AUC Acc. Sp Sn AUC Avg.
Naïve Bayes 91.3 91.7 91.0 0.94 96.0 100 90.9 0.99 91.3 100 81.8 0.95 93.5
VFI 95.6 100 90.0 0.97 95.6 100 90.0 0.97 87.0 83.3 90.0 0.95 93.4
MLP 86.9 91.7 81.8 0.97 95.6 100 90.9 0.98 dnf dnf dnf dnf 92.7*
SVM 95.6 100 90.9 0.96 95.7 100 90.9 0.96 73.9 75.0 72.7 0.74 88.4
Hyper Pipes 95.7 100 90.9 0.99 82.6 91.7 72.7 0.90 78.2 83.3 72.7 0.83 86.6
Logistic R. 86.0 91.7 81.8 0.96 95.7 100 90.9 0.92 69.6 83.3 54.5 0.76 84.8
KNN 91.3 100 81.8 0.92 91.3 100 81.8 0.94 65.2 66.7 63.6 0.72 83.3
Bayes Net 95.7 100 90.9 0.99 82.6 83.3 81.8 0.92 69.6 66.7 72.7 0.64 83.2
Random Forest 87.0 83.3 90.9 0.93 82.6 83.3 81.8 0.91 69.5 66.7 72.7 0.75 81.4
K means 69.6 83.3 54.5 0.69 95.7 100 90.9 0.95 60.9 63.6 63.6 0.63 75.7
M5P 91.3 91.7 90.9 0.86 65.2 58.3 72.7 0.72 65.2 58.3 72.7 0.56 73.4
LDA 91.3 100 81.8 0.97 65.2 71.7 58.6 0.77 17.4 25.0 100 0.52 69.7
K star 73.9 91.7 54.5 0.93 78.2 100 54.5 0.82 47.8 0.0 100 0.50 68.8
SLR 87.0 83.3 90.9 0.89 73.9 75.0 72.7 0.74 43.5 41.7 45.5 0.45 68.5
J48 69.6 66.7 72.7 0.76 69.6 58.3 81.8 0.77 60.9 58.3 63.6 0.66 68.4
ASC 65.6 66.7 72.7 0.76 69.6 66.7 72.7 0.76 47.8 66.7 27.3 0.49 63.1
Random Tree 73.9 91.7 54.5 0.73 73.9 66.7 81.8 0.74 34.8 33.3 36.4 0.35 60.8

Acc: Accuracy, Sp: Specificity, Sn: Sensitivity, AUC: Area under ROC curve, Avg: Average score in % for each algorithms, dnf: Did not Finish”, * denotes Avg. from 3 significance levels. Measures >90% are marked in bold.

Kukreja et al. BMC Bioinformatics 2012 13:139   doi:10.1186/1471-2105-13-139

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