Table 10 Outcomes of performance evaluation metrics.
Model | Accuracy | Precision | Recall | F1-score | ROC-AUC |
|---|---|---|---|---|---|
Logistic regression | 0.75 | 0.75 | 0.75 | 0.74 | 0.83 |
Decision tree | 0.90 | 0.915 | 0.90 | 0.89 | 0.88 |
Random Forest | 0.80 | 0.808 | 0.80 | 0.79 | 0.89 |
Support Vector Machine | 0.65 | 0.653 | 0.65 | 0.63 | 0.75 |
SGD classifier | 0.85 | 0.881 | 0.85 | 0.84 | 0.87 |
MLP | 0.84 | 0.60 | 0.85 | 0.59 | 0.73 |
DNN | 0.74 | 0.78 | 0.75 | 0.71 | 0.81 |
Proposed KAN | 0.93 | 0.93 | 0.93 | 0.91 | 0.97 |
