Table 3.
Receiver Operating Curve Characteristics for Uncensored and Censored Approaches
| Uncensored |
Feature Selected |
||||||||
|---|---|---|---|---|---|---|---|---|---|
| Algorithm | AUC | SE | 95% CI (Lower) | 95% CI (Upper) | Algorithm | AUC | SE | 95% CI (Lower) | 95% CI (Upper) |
| ANN | 69.19 | 9.86 | 49.86 | 88.52 | ANN | 75.40 | 6.40 | 62.90 | 87.90 |
| SVM | 71.88 | 9.43 | 53.40 | 90.36 | SVM | 74.71 | 6.50 | 62.10 | 87.40 |
| DT | 70.54 | 9.65 | 51.62 | 89.46 | DT | 63.00 | 7.10 | 49.10 | 76.90 |
| LR | 64.29 | 10.54 | 43.63 | 84.95 | LR | 72.87 | 6.60 | 60.00 | 85.80 |
Machine learning versus LR methods for 2-year mortality prognostication in a small, heterogeneous glioma database.
AUC, area under the curve; CI, confidence interval; ANN, artificial neural network; SVM, support vector machine; DT, decision tree; LR, logistic regression.