| Table 2. Performance comparison of Machine Learning Models. | ||||||
| Machine Learning Model | Accuracy | Precision | Recall | F1-score | AUC | Global Ranking |
|---|---|---|---|---|---|---|
| Logistic regression (LR) | 0.727 (3) | 0.700 (3) | 0.727 (3) | 0.690 (3) | 0.741 (1) | 2.6 |
| K-nearest neighbors (KNN) | 0.767 (1) | 0.769 (1) | 0.767 (1) | 0.723 (2) | 0.729 (3) | 1.6 |
| Random forest (RF) | 0.742 (2) | 0.750 (2) | 0.740 (2) | 0.726 (1) | 0.733 (2) | 1.8 |
| Support vector machine (SVM) | 0.685 (4) | 0.677 (4) | 0.682 (4) | 0.671 (4) | 0.704 (4) | 4.0 |
| Multilayer perceptron (MLP) | 0.658 (5) | 0.671 (5) | 0.662 (5) | 0.650 (5) | 0.637 (5) | 5.0 |
Data are presented as mean (ranking value).
Ranking value: KNN (1) > RF > LR > SVM > MLP (5).