  Currently, approximately 30% of epileptic patients treated with antiepileptic
drugs (AEDs) remain resistant to treatment (known as refractory patients). This
project seeks to understand the underlying similarities in refractory patients
vs. other epileptic patients, identify features contributing to drug resistance
across underlying phenotypes for refractory patients, and develop predictive
models for drug resistance in epileptic patients. In this study, epileptic
patient data was examined to attempt to observe discernable similarities or
differences in refractory patients (case) and other non-refractory patients
(control) to map underlying mechanisms in causality. For the first part of the
study, unsupervised algorithms such as Kmeans, Spectral Clustering, and
Gaussian Mixture Models were used to examine patient features projected into a
lower dimensional space. Results from this study showed a high degree of
non-linearity in the underlying feature space. For the second part of this
study, classification algorithms such as Logistic Regression, Gradient Boosted
Decision Trees, and SVMs, were tested on the reduced-dimensionality features,
with accuracy results of 0.83(+/-0.3) testing using 7 fold cross validation.
Observations of test results indicate using a radial basis function kernel PCA
to reduce features ingested by a Gradient Boosted Decision Tree Ensemble lead
to gains in improved accuracy in mapping a binary decision to highly non-linear
features collected from epileptic patients.
