Özet:

Epilepsy disease, a neurological disorder that causes recurrent and sudden crises, occurs at unforeseen times. This study presents the classification of electroencephalogram signals for epileptic seizure prediction. The performances of the machine learning algorithms are evaluated on the dataset extracted from electroencephalogram signals. The dataset consists of 500 instances which have 4097 data points for 23.5 seconds. Since the dataset unbalanced, Random Under Sampling and Random Over Sampling methods are performed on this dataset. Therefore, this study is conducted on three datasets. Each dataset is split to 60% train - 40% test, 70% train - 30% test and 80% train - 20% test within the three scenarios. The performances of Diagonal Linear Discriminant Analysis, Linear Discriminant Analysis, Logistic Regression and Random Forest machine learning algorithms on these datasets are assessed, and discussed. The overall results show that Random Forest is the superior algorithm for all datasets in terms of accuracy, sensitivity and specificity metrics.

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