Efficient Approach to Detect Epileptic Seizure using Machine Learning Models for Modern Healthcare System

Abstract

Epileptic seizure is one of the common neurological disorder now a day. But this is curable if it can be detected in the early stage. So, this research become a necessity in the early prediction of epileptic seizure. A complete and reliable system can classify the epileptic seizure patients and the states of epileptic seizure. This research explores a supervised machine learning and deep learning model for the classification of epileptic seizure patients from the Epileptic Seizure dataset of UCI machine learning repository. The dataset has 11,500 instances; every information contains 178 attributes. XGBoost is used for the Machine learning approach and ANN is used for Deep learning approach. The proposed ANN algorithm has the improved accuracy and accurately classified the epileptic seizure class patients. 10-fold cross validation is used for the validation purpose. XGBoost acquires 96.6% test accuracy and ANN acquires 98.26% test accuracy. The proposed Deep Learning approach has out-performed the conventional epileptic seizure classifier algorithms. Additionally, the Deep learning model enhances the performance of epileptic seizure detection.