Table 8

Performance measures of data mining algorithm at different levels of significance on B & D 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 91.7 100 83.3 0.95 91.7 91.7 91.7 0.92 95.8 91.7 100 0.98 93.6
SVM 91.7 100 83.3 0.92 91.7 91.7 91.7 0.92 95.8 100 91.7 0.96 93.1
VFI 87.5 100 75.0 0.93 91.7 100 83.3 0.94 95.8 100 91.7 1.00 92.7
Logistic R. 79.1 83.3 75.0 0.92 100 100 100 1.00 87.5 91.7 83.3 0.97 90.7
MLP 87.5 91.7 83.3 0.94 87.5 83.3 91.7 0.96 dnf dnf dnf dnf 89.3*
K means 87.5 91.7 83.3 0.88 91.4 91.7 91.7 0.92 87.5 83.3 91.7 0.88 89.0
Hyper Pipes 87.5 83.3 91.7 0.89 91.7 91.7 91.7 0.87 83.3 75.0 91.7 0.90 87.8
Bayes Net 83.3 83.3 83.3 0.89 87.5 91.7 83.3 0.86 83.3 83.3 83.3 0.84 85.1
SLR 83.3 83.3 83.3 0.88 79.2 66.7 91.7 0.90 87.5 100 75.0 0.89 84.7
KNN 79.2 75.0 83.3 0.80 83.3 83.3 83.3 0.83 87.5 91.7 83.3 0.90 83.6
Random Forest 83.3 83.3 83.3 0.83 79.2 83.3 75.0 0.84 79.2 83.3 75.0 0.81 81.1
M5P 87.5 91.7 83.3 0.88 79.2 83.3 75.0 0.73 75.0 83.3 66.7 0.69 79.6
ASC 91.7 100 83.3 0.83 75.0 83.3 66.7 0.61 70.8 75.0 66.7 0.64 76.7
J48 91.7 100 83.3 0.83 75.0 83.3 66.7 0.61 70.8 75.0 66.7 0.64 76.7
Random Tree 83.3 91.7 75.0 0.83 70.8 66.7 75.0 0.71 70.8 66.7 75.0 0.71 75.0
K star 70.8 66.7 75.0 0.83 79.2 75.0 83.3 0.82 58.3 100 16.7 0.58 70.7
LDA 62.5 72.3 60.9 0.75 50.0 65.0 48.0 0.71 20.8 42.6 18.6 0.45 52.6

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