Table 4 The performance of employed optimized machine learning approach in oversampling experiment based on the predictor’s importance and the number of predictors
# Predictors | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC | Selected ML Model |
|---|---|---|---|---|---|
1 | 71.11 | 77.78 | 66.67 | 0.728 | Tree |
2 | 91.11 | 100 | 85.19 | 0.907 | SVM |
3 | 100 | 100 | 100 | 1.000 | SVM |
4 | 100 | 100 | 100 | 1.000 | SVM |
5 | 100 | 100 | 100 | 1.000 | SVM |
6 | 100 | 100 | 100 | 1.000 | SVM |
7 | 100 | 100 | 100 | 1.000 | SVM |
8 | 95.56 | 88.89 | 100 | 1.000 | SVM |
9 | 95.56 | 88.89 | 100 | 1.000 | SVM |
10 | 100 | 100 | 100 | 1.000 | SVM |
11 | 100 | 100 | 100 | 1.000 | SVM |
12 | 100 | 100 | 100 | 1.000 | SVM |
13 | 100 | 100 | 100 | 1.000 | SVM |
14 | 100 | 100 | 100 | 1.000 | SVM |
15 | 100 | 100 | 100 | 1.000 | Naive Bayes |
16 | 100 | 100 | 100 | 1.000 | SVM |
17 | 100 | 100 | 100 | 1.000 | SVM |
18 | 100 | 100 | 100 | 1.000 | SVM |
19 | 100 | 100 | 100 | 1.000 | SVM |
20 | 100 | 100 | 100 | 1.000 | SVM |
21 | 100 | 100 | 100 | 1.000 | SVM |
22 | 100 | 100 | 100 | 1.000 | SVM |
23 | 100 | 100 | 100 | 1.000 | SVM |
24 | 100 | 100 | 100 | 1.000 | SVM |
25 | 100 | 100 | 100 | 1.000 | SVM |