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.

Classification, Electroencephalogram, Epilepsy, Machine Learning, Regression

[1] J.V.N. Lakshmi, Ananthi Sheshasaayee, “A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques”, International Journal of scientific Research in Computer Science and Engineering, vol 5, issue 3, pp 92-97, June (2017), E-ISSN : 2320-7639
[2] Md. Khayrul Bashar, Ishio Chiaki, Hiroaki Yoshida Human Identification from Brain EEG signals Using Advanced Machine Learning Method, 978-1-4673-7791-1/16/$31.00 ©2016 IEEE
[3] Sabrina Ammar, Mohamed Senouci, “Seizure Detection with Single-Channel EEG using Extreme Learning Machine” 978-1-5090-3407-9/16/$31.00 ©2016 IEEE
[4] Vinay K, ”Machine learning approach via an ensemble of classifiers for computer aided lung nodule diagnosis”, Shodhganga :a reservoir of Indian theses @ INFLIBNET, Mar2015
[5] Rishi Das Roy, “Development and application of machine learning tool in deciphering biological information”, Shodhganga :a reservoir of Indian theses @ INFLIBNET, June 2016
[6] Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, Mohammed K. Ali Shatnawi, ‘Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm’ International Journal of Business, Humanities and Technology Vol. 3 No. 3; March 2013 32
[7] Aman Kataria1, M. D. Singh, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 6, June 2013)
[8] Chin-Min Ma, Wei-Shui Yang and Bor-Wen Cheng, ‘How the parameters of K-Nearest Neighbor Algorithm Impact on the Classification Accuracy: In Case of Parkinson Dataset’ Journal of Applied Sciences 14 (2): 171-176,2014 ISSN 1812-5654 / DOI: 10.3923/ jas 2014.171.176 ©2014 Asian Network for Science Information
[9] Weiting Chen, Yu Wang, Guitao Cao, Guoqiang Chen, Qiufang Gu, December 2013 ‘A random forest model based classification scheme for neonatal amplitude-integrated EEG’ IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2013) Shanghai, China. 18-21
[10] Swati Vaid, Preeti Singh and Chamandeep Kaur, “Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique”’, Indian Journal of Science and Technology, Vol 8(28), DOI: 10.17485/ijst/2015/v8i28/70797, ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 October 2015
[11] Meenakshi Dauwan, Jessica J. van der Zande, Edwin van Dellen, Iris E. C. Sommer, Philip Schelten, Afina W. Lemstra, Cornelis J. Stam, “Random Forest to differentiate dementia with lewy bodies from alzheimers disease”, 2352-8729/Ó2016 Published by Elsevier Inc
[12] Juliano Machado, Alexandre Balbinot, Adalberto schuck “A study of Naïve Bayes Classifier for analyzing imaginary movement EEG signal using the periodogram as spectral estimation”, 2013
[13] Beata szuflitowska, przemyslaw, orlowski, “Comparision of the EEG signal classifier LDA, NBC and GNBC based on time frequency features”, 2017
[14] Ali Akbar Hossinezadeh, Azra Yoghoobi Karimoi, Reza Yaghoobi, Mohammad Ali Khalizadeh, “EEG signal classification using Bayes and Naïve Bayes classifier and extracted features of continues wavelet transform”
[15] Khalid Alkhatib, Hassan Najadat, Ismail Hmeidi, Mohammed K. Ali Shatnawi, “Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm” International Journal of Business, Humanities and Technology Vol. 3 No. 3; March 2013 32
[16] Deepika Mallampati, “An Efficient Spam Filtering using Supervised Machine Learning Techniques”, International Journal of scientific Research in Computer Science and Engineering, vol 6, issue 2, pp 33-37, April (2018), E-ISSN : 2320-7639
[17] Zhongheng Zhang, Submitted, “ Naïve Bayes classification in R”, Accepted for publication Feb 24, 2016. doi: 10.21037/atm.2016.03.38 View this article at: http://dx.doi.org/10.21037/atm.2016.03.38, Jan 25, 2016