Model performance (AUC) in distinguishing between JME and the other epilepsy subtypes
| JME v. Non-IGE Epilepsies (AUC) | JME v. IGE Epilepsies (AUC) | |||
|---|---|---|---|---|
| Performance metric | RE | ESES | CAE | JAE |
| IGE-PRS | 0.51 ± 0.02 | 0.48 ± 0.02 | 0.50 ± 0.03 | 0.52 ± 0.02 |
| Pruning and thresholding | 0.60 ± 0.03 | 0.50 ± 0.03 | 0.61 ± 0.03 | 0.61 ± 0.04 |
| Elastic net | 0.60 ± 0.04 | 0.62 ± 0.04 | 0.62 ± 0.03 | 0.71 ± 0.04 |
| ePRS | 0.65 ± 0.02 | 0.66 ± 0.03 | 0.65 ± 0.03 | 0.69 ± 0.02 |
[i] IGE-PRS performs no better than random guessing as expected, whereas the ePRS significantly outperforms both IGE-PRS and pruning and thresholding. Although elastic net demonstrates comparable predictive performance with ePRS on average, it exhibits greater variability across different train-test splits, leading to greater uncertainty in its performance on any given data set. Epilepsy subtypes listed include rolandic epilepsy (RE), electrical status epilepticus in sleep (ESES), childhood absence epilepsy (CAE), and juvenile absence epilepsy (JAE). (IGE) idiopathic generalized epilepsy; (JME) juvenile myoclonic epilepsy.