Classification of epileptic seizure dataset using different machine learning algorithms

Seizure associated with abnormal brain activities caused by epileptic disorder is widely typical and has many symptoms, such as loss of awareness and unusual behavior as well as confusion. In this paper, a classification of the Epileptic Seizure dataset was done using different classifiers. It was shown that the Random Forest classifier outperformed K- Nearest Neighbor (K-NN), Naïve Bayes, Logistic Regression, Decision Tree (D.T.), Random Tree, J48, Stochastic Gradient Descent (S.G.D.) classifiers with 97.08% Accuracy, ROC = 0.996, and RMSE = 0.1527. Sensitivity analysis for some of these classifiers was performed to study the performance of the classifier to classify the Epileptic Seizure dataset with respect to some changes in their parameters. Then a prediction of the dataset using feature selection based on attributes variance was also performed.

Abstract licence: CC BY-NC-ND

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