Table 6.
Performance comparison of recent prediction models on Lung Cancer Datasets
| Work Ref. | Classifier | Performance metrics | ||||
|---|---|---|---|---|---|---|
| ACC | P | R | F1 | S | ||
| CIA | ||||||
| Tekade and Rajeswari [218] | VGG | 0.95 | – | – | – | – |
| Anifah et al. [16] | DNN | 0.80 | – | – | – | – |
| Alam et al. [10] | SVM | 0.97 | – | – | – | – |
| Shakeel et al. [198] | DITNN | 0.95 | 0.94 | 0.97 | 0.95 | 0.97 |
| Zhang and Kong [231] | MSDLF | 0.99 | – | – | – | – |
| Mics. Datasets | ||||||
| Jakimovski and Davcev [104] | DNN | 0.75 | – | – | – | – |
| Taher et al. [216] | Rule-based classifier | 0.93 | 0.95 | 0.94 | – | 0.91 |
| Vas and Dessai [222] | DNN | 0.92 | – | 0.88 | – | 0.97 |
| Alzubaidi et al. [13] | SVM | 0.97 | – | 0.96 | – | 0.97 |
| Akter et al. [7] | Neuro-Fuzzy | 0.90 | 0.86 | – | – | 0.81 |