A robust classification model with voting based feature selection for diagnosis of epilepsy

It is well a known fact that neuropsychiatric disorders cause abnormalities in connectivity patterns of brain regions. Identifying and characterising these abnormalities can be exploited to get better diagnosis of neuropsychiatric diseases with help of resting state functional magnetic resonance imaging (rfMRI) data. But this is not an easy task because rfMRI produces data that has very large dimensions that will lead to curse of dimensionality problem. So it is necessary to reduce the number of features in order to get better classification accuracy. This needs a robust feature selection criterion that best describes the differences between epileptic patients and healthy control group. In this paper we present a classification model in which we introduce a voting based feature selection (VFS) approach that ensures the selection of most discriminative features by combining the capabilities of several feature selection techniques. We used AdaBoost for RBF network as a classifier to avoid over fitting. We applied this model on rfMRI-based data to discriminate between two groups. We correctly classify epileptic patients from healthy controls with 85.33% classification accuracy on a heterogeneous data set using the proposed classification model. The results presented in this paper are better than other reported results in the current literature on this dataset to the best of our knowledge confirming the effectiveness of our classification model.