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Proposed classification results with 5‐fold validation strategy on dataset 2 using different machine learning algorithms, FNR is representing false negative rate
| Classifier | Accuracy (%) | FNR (%) | Precision (%) | Sensitivity (%) | Time (s) |
|---|---|---|---|---|---|
| LD | 98.7 | 1.3 | 98.75 | 98.68 | 13.00 |
| QSVM | 98.8 | 1.2 | 98.87 | 98.52 | 35.88 |
| LSVM | 98.5 | 1.5 | 98.67 | 98.13 | 46.00 |
| CSVM | 98.9 | 1.1 | 98.95 | 98.82 | 44.69 |
| MGSVM | 97.4 | 2.6 | 97.49 | 97.11 | 14.10 |
| CKNN | 97.7 | 2.3 | 98.01 | 97.51 | 48.25 |
| LR | 98.6 | 1.4 | 98.88 | 98.20 | 216.8 |
