Table 2 The performance of employed optimized machine learning approach in undersampling experiment based on the predictor’s importance and the number of predictors
# Predictors | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC | Selected ML Model |
|---|---|---|---|---|---|
1 | 44.44 | 33.33 | 66.67 | 0.722 | KNN |
2 | 77.78 | 66.67 | 100 | 1.000 | Naive Bayes |
3 | 66.67 | 66.67 | 66.67 | 0.889 | Ensemble |
4 | 77.78 | 83.33 | 66.67 | 0.944 | Ensemble |
5 | 77.78 | 83.33 | 66.67 | 0.944 | Ensemble |
6 | 100 | 100 | 100 | 1.000 | Ensemble |
7 | 100 | 100 | 100 | 1.000 | Ensemble |
8 | 100 | 100 | 100 | 1.000 | Ensemble |
9 | 100 | 100 | 100 | 1.000 | Ensemble |
10 | 100 | 100 | 100 | 1.000 | Ensemble |
11 | 100 | 100 | 100 | 1.000 | Ensemble |
12 | 100 | 100 | 100 | 1.000 | Ensemble |
13 | 100 | 100 | 100 | 1.000 | Ensemble |
14 | 100 | 100 | 100 | 1.000 | Ensemble |
15 | 100 | 100 | 100 | 1.000 | Ensemble |
16 | 100 | 100 | 100 | 1.000 | Ensemble |
17 | 100 | 100 | 100 | 1.000 | Ensemble |
18 | 100 | 100 | 100 | 1.000 | Ensemble |
19 | 100 | 100 | 100 | 1.000 | Ensemble |
20 | 100 | 100 | 100 | 1.000 | Ensemble |
21 | 100 | 100 | 100 | 1.000 | Ensemble |
22 | 100 | 100 | 100 | 1.000 | Ensemble |
23 | 100 | 100 | 100 | 1.000 | Ensemble |
24 | 100 | 100 | 100 | 1.000 | Ensemble |
25 | 100 | 100 | 100 | 1.000 | Ensemble |