Research Article Open Access

Random Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph Records

Heri Kuswanto1, Mutiah Salamah1 and Muhammad Idrus Fachruddin1
  • 1 Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

Abstract

Complexity in data structure has led to the rapid development of computational statistics methods. Machine learning approaches have been introduced and applied to solve complex problems in many fields. This paper applies two common machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), in the detection of epilepsy. The diagnosis of epilepsy can usually only be made when a seizure is happening, which leads to some difficulties in the diagnostic process. The most recent way of diagnosing epilepsy is by using an Electroencephalograph (EEG) record. However, detecting epilepsy cases through EEG records takes a long time and may lead to misleading diagnostic results. The use of machine learning approaches is intended to generate fast and accurate classification results. As the EEG only generates a signal, direct analysis using RF or SVM cannot be carried out and the EEG record needs to be pre-processed. This paper uses Discrete Wavelet Transform and Line Length Features in the data pre-processing stage to decompose the signal by frequency and time. The classification results show that both RF and SVM perform very well and are able to classify cases of epilepsy accurately. The RF outperforms the SVM in the training dataset, while the SVM has a better performance in testing, with almost nom is classified cases. Several open problems relating to interpretation as well as parameter settings are described.

American Journal of Applied Sciences
Volume 14 No. 5, 2017, 533-539

DOI: https://doi.org/10.3844/ajassp.2017.533.539

Submitted On: 14 January 2016 Published On: 4 May 2017

How to Cite: Kuswanto, H., Salamah, M. & Fachruddin, M. I. (2017). Random Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph Records. American Journal of Applied Sciences, 14(5), 533-539. https://doi.org/10.3844/ajassp.2017.533.539

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Keywords

  • Classification
  • Epilepsy
  • SVM
  • Random Forest