Table 3.
Performance comparison of recent prediction models on WBC datasets
| Work Ref. | Technique Used | Performance metrics | ||||
|---|---|---|---|---|---|---|
| ACC | P | R | F1 | S | ||
| Zheng et al. [234] | K-means aided feature transform + SVM | 0.94 | – | 0.96 | – | 0.92 |
| Kamel et al. [109] | GWO FS + SVM | 1.00 | – | 1.00 | – | 1.00 |
| Mafarja et al. [129] | Hybridized WOA and SA | 0.97 | – | – | – | – |
| Ghosh et al. [66] | SMO-X FS + k-NN | 1.00 | – | – | – | – |
| Huang et al. [98] | GA-RBF SVM (small scale data) | 0.98 | – | – | 0.99 | – |
| RBF-SVM (large scale data) | 0.99 | – | – | 0.99 | – | |
| Kumari and Singh [122] | k-NN | 0.99 | – | – | – | – |
| Kumar et al. [121] | Lazy k-star/ Lazy IBK. | 0.99 | 0.99 | 0.99 | 0.99 | – |
| Prakash and Rajkumar [165] | HLFDA-T2FNN | 0.98 | – | – | – | – |
| Preetha and Jinny [166] | PCA-LDA + ANNFIA | 0.97 | – | – | – | – |
| Sandhiya and Palani [186] | ICRF-LCFS | 0.94 | – | – | – | – |
| Chatterjee et al. [36] | SSD-LAHC + k-NN | 0.99 | – | – | – | – |
| Ahmed et al. [6] | RTHS + k-NN | 0.99 | – | – | – | – |
| Guha et al. [72] | CPBGSA +MLP | 0.99 | – | – | – | – |
| Guha et al. [73] | ECWSA + k-NN | 0.95 | – | – | – | – |
| Ghosh et al. [67] | MRFO + k-NN | 1.00 | – | – | – | – |