Table 11 Detailed metrics for three best performing classifiers on PDA data set.
From: Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis
Classification (feature selection) | Train and validation (avg, %) | Test (%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
ACC | PPV | TPR | TNR | AUC | ASE | ACC | PPV | TPR | TNR | AUC | ASE | |
Neural network (logistic regression) | 85 | 82 | 91 | 79 | 88 | 14 | 90 | 88 | 95 | 86 | 100 | 10 |
Logistic regression (decision tree) | 85 | 82 | 91 | 79 | 81 | 14 | 90 | 88 | 95 | 86 | 100 | 10 |
Logistic regression (gradient boosting) | 85 | 82 | 91 | 79 | 80 | 15 | 90 | 88 | 95 | 86 | 100 | 10 |
