New Approach for Automated Epileptic Disease Diagnosis Using an Integrated Self-Organization Map and Radial Basis Function Neural Network Algorithm

Currently, epilepsy disease (ED) is considered to be one of the gradual diseases in brain function over a period of several months or years. Seizure status is the primary common cause of ED. The main goal of this paper is to discover the seizure and epilepsy status using the prediction algorithm on the test results received from patient medical reports. This paper proposed an automatic epilepsy diagnostic method based on a self-organization map (SOM) method using a radial basis function (RBF) neural networks approach. The hybrid technique sought to enhance epilepsy diagnosis precision and to decrease the misdiagnosis of seizure disease. The SOM algorithm was employed to differentiate the unknown patterns of the seizure and epilepsy dataset. The experiments were performed on various RBF neural network algorithms with integrated SOM algorithms to predict and classify the standard epilepsy disease dataset. The hybrid method was tested on the UCI epilepsy dataset. The overall detection accuracy with 10-fold cross validation using SOM-RBF method achieved 97.47%. The results were compared with other modern classification techniques for seizure prediction and detection in terms of the evaluation factor.