Table 3 Benchmarking various algorithmic approaches
Model | TP | FP | FN | TN | Acc. (%) | Pre. (%) | Rec. (%) | F1-score (%) |
|---|---|---|---|---|---|---|---|---|
KNN | 814 | 170 | 157 | 827 | 83.3 | 82.7 | 83.8 | 83.2 |
NN | 726 | 248 | 207 | 787 | 76.8 | 74.5 | 77.8 | 76.1 |
RF | 735 | 237 | 218 | 718 | 76.8 | 75.6 | 77.1 | 76.3 |
SVM-L | 494 | 487 | 475 | 512 | 51.1 | 50.3 | 50.9 | 50.6 |
SVM-R | 651 | 275 | 219 | 823 | 74.8 | 70.3 | 74.8 | 72.4 |
ADA | 747 | 235 | 168 | 836 | 80.4 | 76.1 | 81.6 | 78.7 |
MNB | 630 | 309 | 226 | 803 | 72.8 | 67.0 | 73.6 | 70.1 |
Proposed method | 942 | 20 | 3 | 1004 | 98.6 | 97.9 | 99.6 | 98.7 |