Epileptic Electroencephalogram Classification Using Machine Learning Algorithms - Rani, T. Perumal; Chellam G, Heren (International Journal of Computer Sciences and Engineering, 2018)
Epilepsy is disease which is caused due to neurological disorder of a brain. It may cause recurrent seizures. It can be detected with the EEG signals and records the activity of brain electrically. In this paper K-Nearest Neighbor, Random Forest and Naive Bayes algorithms are used for classification of Electroencephalogram (EEG) signal as epilepsy or normal signal. These Machine learning algorithms learn directly from the data by experience which is not interrupted manually. Supervised learning uses labeled data for training which maps the input to the corresponding output. It classified into two types such as classification and regression. Classification means prediction of output from the input to which class it relies on, such as boy or girl. Whereas Regression means prediction of output from the input but output is predicted as a real value like measurement of rainfall etc. Here Random Forest method performs the best classification other than that of KNN and Naïve Bayes.