Over fifty million people in the world are affected by a neurological disorder namely Seizures. The seizure occurs due to uncontrolled electrical activities inside the brain. It may cause changes in someone's behaviour, movement, or emotions. Many seizures last for about 30 seconds to two minutes. Electroencephalogram (EEG) signals are used to detect the existence of seizures. The detection of seizures is very important from the patient's point of view. Therefore, there is a need to study and explore machine learning algorithms which may learn various patterns in the EEG datasets and perform accurate classification. In this work, the algorithms like K-nearest neighbour (KNN), Naïve Bayes (NB), Decision tree (DT) and Random forest (RF) are compared for classification of EEG dataset. These algorithms are applied on extracted features which result in the classification of EEG signals and are classified as seizures and non-seizures. After experimentation, it is observed that Random Forest gives best accuracy.
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