Year 2020, Volume 8 , Issue 2, Pages 279 - 285 2020-05-26

Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection
Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection

KEMAL AKYOL [1] , ÜMİT ATİLA [2]


Tekrarlayan ve ani krizlere neden olan nörolojik bir hastalık olan epilepsy hastalığı öngörülemeyen zamanlarda ortaya çıkar. Bu çalışma, epileptik nöbet tahmini için elektroensefalogram sinyallerinin sınıflandırılmasını sunmaktadır. Makine öğrenme algoritmalarının performansı, elektroensefalogram sinyallerinden elde edilen veriseti üzerinde değerlendirilmiştir. Veriseti, 23.5 saniye boyunca 4097 veri noktasına sahip 500 örnek içermektedir. Veriseti dengesiz olduğu için, bu veri setinde Rastgele Alt Örnekleme ve Rastgele Üst Örnekleme yöntemleri uygulanmıştır. Bu nedenle bu çalışma üç veri seti üzerinde yürütülmüştür. Her veri seti üç senaryo çerçevesinde % 60 eğitim - % 40 test, % 70 eğitim - % 30 test ve % 80 eğitim - % 20 test verileri olarak ayrılmıştır. Bu verisetleri üzerinde Çapraz Doğrusal Ayırt Edici Analiz, Doğrusal Ayırt Edici Analiz, Lojistik Regresyon ve Rastgele Orman makine öğrenmesi algoritmaların performansları değerlendirilmiş ve tartışılmıştır. Genel sonuçlar, tüm verisetleri için Random Forest algoritmasının doğruluk, hassasiyet ve özgüllük metrikleri açısından üstün olduğunu göstermiştir.

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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Primary Language en
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-2272-5243
Author: KEMAL AKYOL
Institution: KASTAMONU ÜNİVERSİTESİ
Country: Turkey


Orcid: 0000-0002-1576-9977
Author: ÜMİT ATİLA
Institution: KARABÜK ÜNİVERSİTESİ
Country: Turkey


Dates

Publication Date : May 26, 2020

Bibtex @research article { apjes569553, journal = {Akademik Platform Mühendislik ve Fen Bilimleri Dergisi}, issn = {}, eissn = {2147-4575}, address = {}, publisher = {Academic Platform}, year = {2020}, volume = {8}, pages = {279 - 285}, doi = {10.21541/apjes.569553}, title = {Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection}, key = {cite}, author = {Akyol, KEMAL and Ati̇la, ÜMİT} }
APA Akyol, K , Ati̇la, Ü . (2020). Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection . Akademik Platform Mühendislik ve Fen Bilimleri Dergisi , 8 (2) , 279-285 . DOI: 10.21541/apjes.569553
MLA Akyol, K , Ati̇la, Ü . "Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection" . Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 (2020 ): 279-285 <https://dergipark.org.tr/en/pub/apjes/issue/52467/569553>
Chicago Akyol, K , Ati̇la, Ü . "Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 (2020 ): 279-285
RIS TY - JOUR T1 - Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection AU - KEMAL Akyol , ÜMİT Ati̇la Y1 - 2020 PY - 2020 N1 - doi: 10.21541/apjes.569553 DO - 10.21541/apjes.569553 T2 - Akademik Platform Mühendislik ve Fen Bilimleri Dergisi JF - Journal JO - JOR SP - 279 EP - 285 VL - 8 IS - 2 SN - -2147-4575 M3 - doi: 10.21541/apjes.569553 UR - https://doi.org/10.21541/apjes.569553 Y2 - 2020 ER -
EndNote %0 Akademik Platform Mühendislik ve Fen Bilimleri Dergisi Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection %A KEMAL Akyol , ÜMİT Ati̇la %T Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection %D 2020 %J Akademik Platform Mühendislik ve Fen Bilimleri Dergisi %P -2147-4575 %V 8 %N 2 %R doi: 10.21541/apjes.569553 %U 10.21541/apjes.569553
ISNAD Akyol, KEMAL , Ati̇la, ÜMİT . "Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection". Akademik Platform Mühendislik ve Fen Bilimleri Dergisi 8 / 2 (May 2020): 279-285 . https://doi.org/10.21541/apjes.569553
AMA Akyol K , Ati̇la Ü . Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection. APJES. 2020; 8(2): 279-285.
Vancouver Akyol K , Ati̇la Ü . Comparing The Effect of Under-Sampling and Over-Sampling on Traditional Machine Learning Algorithms for Epileptic Seizure Detection. Akademik Platform Mühendislik ve Fen Bilimleri Dergisi. 2020; 8(2): 279-285.