Table 22 The proposed methods compared with the hybrid of GA and RFE
Datasets | Train Data % | Test Data % | Over-fitting Diff. % | Pre | Rec | F1-score | NO.F | F-Time (sec) | C-Time (sec) | AUC | Var. | ACC % |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVM classifier | ||||||||||||
 RNA gene | 99.791 | 99.750 | 0.221 | 0.999 | 0.997 | 0.998 | 3123 | 15,746.043 | 0.727 | 1.000 | 0.000028 | 99.750 |
 DNA CNV | 93.271 | 84.980 | 8.291 | 0.860 | 0.770 | 0.790 | 2940 | 62,405.810 | 35.118 | 0.965 | 0.000620 | 84.980 |
 Parkinson’s disease | 75.529 | 74.996 | 0.533 | 0.530 | 0.523 | 0.474 | 149.000 | 55.114 | 0.071 | 0.768 | 0.000652 | 74.996 |
 Dermatology diseases | 84.800 | 84.722 | 0.078 | 0.854 | 0.835 | 0.830 | 5.000 | 0.651 | 0.016 | 0.960 | 0.000052 | 84.722 |
 BreastEW | 91.799 | 91.394 | 0.405 | 0.463 | 0.420 | 0.439 | 5.000 | 0.656 | 0.016 | 0.977 | 0.000776 | 91.394 |