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

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.

Anahtar Kelimeler:

Epileptik nöbet, makine öğrenmesi, dengesiz ve dengeli veri seti, üst örnekleme, alt örnekleme.

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

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.

Keywords:

Epileptic seizure, machine learning, unbalanced and balanced dataset, over sampling, under sampling.,

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