Table 4 Comparing the performance of CNFE-SE with other state of the art classifiers
Feature set | Classifier | Accuracy | Sensitivity | Specificity | AUC | F Score |
|---|---|---|---|---|---|---|
All 296 features | RF | 0.58 ± 0.01 | 0.69 ± 0.05 | 0.46 ± 0.06 | 0.58 ± 0.01 | 0.55 ± 0.05 |
|  | DT | 0.55 ± 0.01 | 0.62 ± 0.04 | 0.49 ± 0.04 | 0.55 ± 0.01 | 0.55 ± 0.04 |
|  | NB | 0.53 ± 0.01 | 0.79 ± 0.11 | 0.26 ± 0.12 | 0.54 ± 0.01 | 0.39 ± 0.11 |
|  | ANN | 0.50 ± 0.01 | 0.54 ± 0.16 | 0.45 ± 0.16 | 0.50 ± 0.01 | 0.49 ± 0.16 |
|  | SVM | 0.54 ± 0.01 | 0.28 ± 0.1 | 0.8 ± 0.09 | 0.56 ± 0.01 | 0.41 ± 0.05 |
|  | XGboost | 0.55 ± 0.01 | 0.53 ± 0.03 | 0.56 ± 0.03 | 0.55 ± 0.01 | 0.54 ± 0.03 |
|  | LGBM | 0.60 ± 0.01 | 0.59 ± 0.03 | 0.59 ± 0.01 | 0.64 ± 0.01 | 0.59 ± 0.02 |
|  | Adaboost | 0.59 ± 0.01 | 0.69 ± 0.02 | 0.48 ± 0.02 | 0.60 ± 0.01 | 0.56 ± 0.02 |
|  | CNFE-SE without FE | 0.71 ± 0.01 | 0.69 ± 0.01 | 0.73 ± 0.01 | 0.71 ± 0.01 | 0.71 ± 0.01 |
|  | CNFE-SE with FE | 0.85 ± 0.01 | 0.79 ± 0.01 | 0.91 ± 0.01 | 0.84 ± 0.01 | 0.85 ± 0.01 |
Only most important features | RF | 0.60 ± 0.02 | 0.69 ± 0.03 | 0.50 ± 0.02 | 0.59 ± 0.02 | 0.60 ± 0.02 |
|  | DT | 0.57 ± 0.03 | 0.63 ± 0.01 | 0.54 ± 0.04 | 0.57 ± 0.02 | 0.58 ± 0.03 |
|  | NB | 0.54 ± 0.01 | 0.52 ± 0.01 | 0.57 ± 0.01 | 0.54 ± 0.01 | 0.54 ± 0.01 |
|  | ANN | 0.54 ± 0.01 | 0.55 ± 0.01 | 0.52 ± 0.01 | 0.53 ± 0.01 | 0.53 ± 0.01 |
|  | SVM | 0.58 ± 0.01 | 0.51 ± 0.01 | 0.70 ± 0.01 | 0.60 ± 0.01 | 0.61 ± 0.01 |
|  | XGboost | 0.58 ± 0.01 | 0.57 ± 0.01 | 0.59 ± 0.01 | 0.58 ± 0.02 | 0.58 ± 0.01 |
|  | LGBM | 0.62 ± 0.02 | 0.61 ± 0.02 | 0.63 ± 0.03 | 0.62 ± 0.02 | 0.62 ± 0.02 |
|  | Adaboost | 0.62 ± 0.01 | 0.69 ± 0.01 | 0.51 ± 0.01 | 0.61 ± 0.01 | 0.60 ± 0.01 |
|  | CNFE-SE without FE | 0.72 ± 0.01 | 0.71 ± 0.01 | 0.74 ± 0.01 | 0.72 ± 0.01 | 0.72 ± 0.01 |
|  | CNFE-SE with FE | 0.87 ± 0.01 | 0.82 ± 0.01 | 0.92 ± 0.01 | 0.87 ± 0.01 | 0.87 ± 0.01 |