Table 6

Performance measures of data mining algorithm at different levels of significance on A & B 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 87.5 83.3 91.7 0.84 91.7 83.3 100 0.97 91.7 83.3 100 0.96 90.8
VFI 79.2 75.0 83.3 0.93 91.7 83.3 100 0.95 87.5 75.0 100 0.90 87.7
K means 87.5 83.3 91.7 0.88 91.7 83.3 100 0.92 83.3 75.0 91.7 0.83 87.5
SVM 83.3 83.3 83.3 0.83 87.5 91.7 83.3 0.87 87.5 83.3 91.7 0.88 86.1
MLP 79.2 83.3 75.0 0.70 91.7 91.7 91.7 0.95 dnf dnf dnf dnf 84.7*
Hyper Pipes 83.3 75.0 91.7 0.91 83.3 83.3 83.3 0.93 70.8 83.3 58.3 0.88 82.0
Logistic R. 66.7 83.3 50.0 0.76 95.8 91.7 100 0.92 79.2 83.3 75.0 0.85 81.5
Random Forest 79.2 83.3 75.0 0.91 79.2 75.0 83.3 0.86 79.2 75.0 83.3 0.78 80.6
Bayes Net 83.3 75.0 91.7 0.87 83.3 83.3 83.3 0.83 75.0 75.0 75.0 0.67 80.2
KNN 75.0 83.3 66.7 0.85 75.0 91.7 58.3 0.90 75.0 91.7 58.3 0.84 77.8
M5P 75.0 83.3 66.7 0.74 75.0 75.0 75.0 0.79 75.0 75.0 75.0 0.74 75.2
ASC 62.5 66.7 58.3 0.65 79.2 83.3 75.0 0.85 70.8 75.0 66.7 0.76 72.0
J48 62.5 66.7 58.3 0.65 79.2 83.3 75.0 0.85 66.7 75.0 58.3 0.72 70.6
Random Tree 70.8 75.0 66.7 0.70 70.8 75.0 66.7 0.70 66.7 66.7 66.7 0.67 69.3
SLR 70.8 75.0 66.7 0.80 66.7 75.0 58.3 0.77 50.0 50.0 50.0 0.60 65.0
K star 66.7 91.7 41.7 0.83 58.3 100 46.7 0.83 50.0 0.0 100 0.50 64.3
LDA 79.2 83.3 75.0 0.84 61.2 64.5 54.5 0.52 29.2 14.3 100 0.56 62.8

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.

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

Open Data