Table 2.
The comparison of prediction performance between LR, SVM (linear kernel and RBF kernel), BP neural network, and Siamese-KNN
| Methods | |||||
|---|---|---|---|---|---|
| Metric | LR | SVM (linear) | SVM (RBF) | BPNN | Siamese-KNN |
| LOOCV accuracy | 64.86% | 64.86% | 67.56% | 72.79% | 83.78% |
| Sensitivity | 57.89% | 57.89% | 63.16% | 68.42% | 84.21% |
| Specificity | 72.22% | 72.22% | 72.22% | 77.78% | 83.33% |
| AUC | 0.73 | 0.72 | 0.68 | 0.77 | 0.84 |
| McNemar’s test (P) | 0.016 | 0.039 | 0.031 | 0.125 | — |
Note: Abbreviations: LR, logistic regression; SVM, support vector machine; RBF, radial basis function; BPNN, back-propagation neural network; KNN, K-nearest neighbor; LOOCV, leave-one-out cross-validation; AUC, area under the curve.