Evaluation of Machine Learning Classifiers for Refractory Epilepsy Classification

Sammanfattning: Epilepsy is a neurological disease, where up to 40% of patients, known as having refractory epilepsy, do not become seizure-free through antiepileptic drugs (AEDs). Epilepsy surgery has the highest possibility of treating patients with refractory epilepsy, however, many are never referred to surgical evaluation. Therefore, it is important to identify those patients with refractory epilepsy who should be offered surgical evaluation. This study evaluates to what extent five different machine learning classifiers can accurately classify patients with non-refractory epilepsy, patients with refractory epilepsy not in the surgical registry, and patients with refractory epilepsy in the surgical registry. We trained the classifiers logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), using a surrogate data with 83 features. An evaluation with various metrics showed that all classifiers performed significantly better than chance, with GBM performing to the highest extent with an accuracy score of 97.78% and a recall of 97.33%. Furthermore, we investigated a potential factor that could limit the predictive performance of our classifiers. Through visualization of our data, it was found that patients with refractory epilepsy in the surgical registry and those with refractory epilepsy not in the surgical registry share similar characteristics. Therefore, this limited the classifiers predictive performance as it was more challenging to distinguish between these two groups. In addition, it is of high clinical utility to obtain those features that are considered highly correlated with refractory epilepsy. Therefore, we found that the number of video-intensive extracranial EEG monitoring, and the number of MRI scans of the brain, were among the most important features according to all five classifiers. Our findings imply that GBM has the potential to identify all patients with refractory epilepsy who should be offered surgical evaluation, thus helping in closing the inequality gap.