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.