Table 2. Summary of CR Prediction Analysis.
Maximum achievable performance (MAP) in both training and validation sets for all four datasets using seven different class prediction methods. MAP is defined as highest accuracy achieved by one of seven classifiers methods used for a given dataset.
| a: Class Prediction Results – Maximum Achievable Accuracy – Training Set | ||||||
|---|---|---|---|---|---|---|
| Prediction Method |
Sensitivity | Specificity | PPV | NPV | Accuracy | |
| IFM I | KNN | 24 | 77 | 33 | 69 | 60 |
| IFM II | KNN | 72 | 56 | 65 | 64 | 65 |
| HOVON | SVM | 17 | 92 | 48 | 71 | 70 |
| Mulligan et al. | KNN | 71 | 67 | 68 | 71 | 69 |
| b: Class Prediction Results – Maximum Achievable Accuracy – Test Set | ||||||
|---|---|---|---|---|---|---|
| Prediction Method |
Sensitivity | Specificity | PPV | NPV | Accuracy | |
| IFM I | KNN | 27 | 80 | 40 | 69 | 62 |
| IFM II | KNN | 67 | 56 | 25 | 88 | 58 |
| HOVON | SVM | 31 | 90 | 45 | 83 | 78 |
| Mulligan et al. | KNN | 50 | 36 | 48 | 39 | 44 |
SVM: Support Vector Machines; KNN: K-nearest neighbors (n=1)