Table 2.
Top five models on each feature set and mean (SD) of AUC.
| Rank |
Whole tumour |
Single largest slice |
||||
|---|---|---|---|---|---|---|
| Model | AUC | Optimal feature subset | Model | AUC | Optimal feature subset | |
| 1 | lasso_corr | 0.924 (0.049) | 8 | mlp_corr | 0.914 (0.059) | 20 |
| 2 | svmRad_pca | 0.922 (0.046) | 8 | svmPoly_corr | 0.912 (0.065) | 20 |
| 3 | rf_pca | 0.922 (0.044) | 7 | mlp_full | 0.910 (0.053) | 36 |
| 4 | ridge_corr | 0.921 (0.051) | 19 | lasso_corr | 0.910 (0.065) | 6 |
| 5 | mlp_corr | 0.919 (0.053) | 19 | ridge_corr | 0.909 (0.068) | 20 |
lasso: least absolute shrinkage and selection operator; svmRad: support vector machine with a radial kernel; rf: random forest; ridge: ridge regression; mlp: multilayer perceptron; svmpoly: support vector machine with a polynomial kernel; corr: high correlation filter; PCA: principal components analysis; SD: standard deviation; AUC: area under the curve.