Table 6 Performance metrics of classification models.
From: Efficient diagnosis of diabetes mellitus using an improved ensemble method
Statistical analysis | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) | MCC (%) | AUC_ROC (%) | AUC_PR (%) |
|---|---|---|---|---|---|---|---|
Random forest | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
CART | 78 | 0.79 | 0.90 | 0.84 | 50.46 | 84.42 | 70.80 |
J48 | 78 | 0.79 | 0.90 | 0.84 | 50.46 | 84.21 | 70.45 |
Decision stump | 76 | 0.79 | 0.85 | 0.82 | 45.40 | 71.95 | 54.24 |
AdaBoost | 81 | 0.81 | 0.92 | 0.86 | 56.51 | 89.24 | 80.66 |
Gradient boosting | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
XGBoost | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
