Table 2.
Diagnostic performance of the best performing machine learning model in the training set and the test set.
| Feature selection | No. of features | Classification + subsampling | Training set | Test set | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| AUC (95% CI) | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC (95% CI) | Accuracy (%) | Sensitivity (%) | Specificity (%) | |||
| TLE vs HC | ||||||||||
| LASSO | 16 | SVM + none | 0.920 (0.870–0.970) | 80.5 | 85.7 | 80 | 0.848 (0.731–0.964) | 84.8 | 76.2 | 75 |
| Right TLE vs HC | ||||||||||
| F-score | 30 | LR + SMOTE | 0.920 (0.870–0.970) | 81.1 | 94 | 68.5 | 0.845 (0.723–0.968) | 77.4 | 72.7 | 80 |
| Left TLE vs HC | ||||||||||
| LASSO | 18 | LR + none | 0.935 (0.893–0.977) | 87.8 | 82.5 | 93 | 0.840 (0.699–0.981) | 73.3 | 70 | 75 |
AUC area under the curve, CI confidence interval, HC healthy control, LASSO least absolute shrinkage and selection operator, LR logistic regression, MI mutual information, SMOTE synthetic minority over-sampling technique, SVM support vector machine, TLE temporal lobe epilepsy.