Table 2 Evaluation of BC databases on different eas.
From: Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection
Data | Methods | Parameters | Train Accuracy | Test Accuracy | Runtime(secs) | |
|---|---|---|---|---|---|---|
σ | c | |||||
WBCD | Single SVM | 0.993 | 5.342 | 98.87 | 97.65 | 1022.43 |
DE-SVM | 0.892 | 4.987 | 98.32 | 98.01 | 995.45 | |
GA-SVM | 0.873 | 4.556 | 98.23 | 98.12 | 890.32 | |
CS-SVM | 0.786 | 3.654 | 96.34 | 95.99 | 1011.34 | |
GOA-SVM | 0.432 | 3.112 | 92.54 | 91.57 | 889.04 | |
SeQTLBOGA | 0.065 | 1.443 | 98.95 | 98.34 | 877.98 | |
WDBC | Single SVM | 0.889 | 5.453 | 89.32 | 88.11 | 1088.43 |
DE-SVM | 0.765 | 3.554 | 87.45 | 85.43 | 1011.43 | |
GA-SVM | 0.544 | 3.654 | 88.11 | 86.98 | 899.90 | |
CS-SVM | 0.435 | 3.987 | 89.23 | 87.22 | 880.54 | |
GOA-SVM | 0.323 | 1.342 | 89.32 | 88.42 | 911.32 | |
SeQTLBOGA | 0.034 | 1.123 | 97.43 | 94.19 | 544.89 | |
