Table 6 Results of test dataset for choice of the initial treatment model
Particular | Data | ||
|---|---|---|---|
Model name | Choice of initial treatment | ||
No. of samples (rows) | 289 | 289 | 289 |
Total number of independent variables (columns) | 268 | 268 | 268 |
ML algorithm used | Random Forest | KSVM | XGBoost |
Feature selection technique | SFFS | SFFS | SFFS |
No. of independent variables used in the dataset | 34 | 53 | 46 |
Mean accuracy train | 0.94 | 0.92 | 0.95 |
Mean accuracy test | 0.84 | 0.75 | 0.77 |
Sensitivity | 0.85 | 0.81 | 0.85 |
Specificity | 0.85 | 0.69 | 0.78 |
Mean F-Score train label 0 | 0.95 | 0.93 | 0.96 |
Mean F-Score train label 1 | 0.94 | 0.92 | 0.95 |
Mean F-score test label 0 | 0.85 | 0.76 | 0.78 |
Mean F-score test label 1 | 0.84 | 0.73 | 0.75 |
No. of samples for class-0 | 152 | 152 | 152 |
No. of samples for class-1 | 137 | 137 | 137 |
Base algorithm | Random forest | KSVM | XGBoost |
ROC_AUC_Score | 0.89 | 0.83 | 0.88 |