Table 6 Findings of optimization methods MLP model for the classifying lung cancer.
From: Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification
Models | Accuracy | Sensitivity (TRP) | Specificity (TNP) | Pvalue (PPV) | Nvalue (NPV) | F-Score | AUC |
|---|---|---|---|---|---|---|---|
GGO + MLP | 0.983837 | 0.977337 | 0.990237 | 0.989957 | 0.977961 | 0.983607 | 0.993 |
SC + MLP | 0.966184 | 0.966387 | 0.965986 | 0.965035 | 0.967302 | 0.96571 | 0.975 |
GWO + MLP | 0.960879 | 0.961003 | 0.960758 | 0.959666 | 0.96206 | 0.960334 | 0.972 |
PSO + MLP | 0.951087 | 0.949106 | 0.95302 | 0.951724 | 0.950469 | 0.950413 | 0.964 |
WOA + MLP | 0.94086 | 0.949106 | 0.932983 | 0.931174 | 0.950469 | 0.940054 | 0.957 |
FOA + MLP | 0.937287 | 0.943912 | 0.930707 | 0.931174 | 0.943526 | 0.9375 | 0.953 |
MVO + MLP | 0.927978 | 0.933694 | 0.921986 | 0.926174 | 0.9299 | 0.929919 | 0.937 |
