Doğrusal Olmayan Analiz ve Makine Öğrenimi Kullanılarak Öksürük Seslerinden COVID-19'un Tanımlanması

Year 2021, Issue: 28, 710 - 716, 30.11.2021

Fatma Zehra Solak

https://doi.org/10.31590/ejosat.1010723

Cited By: 1

Abstract

COVID-19'un otomatik teşhisi, insanlarla etkileşimi en aza indirerek hastalığın yayılmasını azaltmada aktif bir role sahiptir. Çeşitli sinyal ve görüntüleri kullanan makine öğrenmesi modelleri, otomatik tanılamanın temelini oluşturur. Bu çalışma, COVID-19 ve COVID-19 değil olarak etiketlenmiş öksürük ses sinyallerini içeren 'Virufy' veri setini kullanarak COVID-19 enfeksiyonunu tespit etmek için makine öğrenmesi tabanlı modeller sunmaktadır. Veri setindeki COVID pozitif öksürük sayısı, COVID negatif olanlardan daha az olduğu için çalışmada öncelikle ADASYN aşırı örnekleme tekniği ile veri dengeleme yapılmıştır. Ardından, Çokfraktallı Eğimden Arındırılmış Dalgalanma Analizi (Multifraktal Detrended Fluctuation Analysis - MDFA), Lempel-Ziv Karmaşıklığı (Lempel–Ziv Complexity-LZC) ve entropi ölçümleri kullanılarak öksürük seslerinin doğrusal olmayan analizi ile öznitelikler çıkarılmıştır. Daha sonra ReliefF yöntemi ile en etkili öznitelikler seçilmiştir. Son olarak, öksürük seslerini COVID-19 veya değil olarak tanımlamak için, Radyal Tabanlı Çekirdek fonksiyona sahip Destek vektör Makineleri (Support Vector Machine with Radial Basis Function-SVM-RBF), Rastgele Orman (Random Forest-RF), Adaboost, Yapay Sinir Ağları (Artificial Neural Network -ANN), k En Yakın Komşuluk (k Nearest Neighbor -kNN) olmak üzere beş makine öğrenme algoritması kullanılmıştır. Çalışma sonucunda, radyal tabanlı çekirdek fonksiyonuna sahip destek vektör makinesi ve seçilen etkin öznitelikler sayesinde COVID-19 hastalarının ve COVID19 olmayan deneklerin öksürük sesleri %95.8 sınıflandırma doğruluğu ile belirlenmiştir. Bu sınıflandırıcı ile %93.1 duyarlılık, %98.6 özgüllük, %98.6 kesinlik, 0.92 kappa istatistik değerleri ve %93.2 ROC eğrisi altında kalan alan değeri elde edilmiştir.

Keywords

COVID-19, Öksürük Sesleri, Entropi, Makine Öğrenimi, MDFA, LZC, SVM.

Identification of COVID-19 from Cough Sounds Using Non-Linear Analysis and Machine Learning

Abstract

Automatic diagnosis of COVID-19 has an active role in reducing the spread of the disease by minimizing interaction with people. Machine learning models using various signals and images form the basis of automatic diagnosis. This study presents the machine learning based models for detecting COVID-19 infection using ‘Virufy’ dataset containing cough sound signals labeled as COVID-19 and Non-COVID-19. Since the number of COVID positive coughs in the set is less than those of COVID negative, firstly, data balancing was performed with the ADASYN oversampling technique in the study. Then, features were extracted by non-linear analysis of cough sounds using Multifractal Detrended Fluctuation Analysis (MDFA), Lempel–Ziv Complexity (LZC) and entropy measures. Later, the most effective features were selected by ReliefF method. Finally, five machine learning algorithms, namely Support Vector Machine with Radial Basis Function (SVM-RBF), Random Forest (RF), Adaboost, Artificial Neural Network (ANN), k Nearest Neighbor (kNN) were used to identify cough sounds as COVID-19 or Non-COVID19. As a result of the study, the cough sounds of COVID-19 patients and Non-COVID19 subjects were identified with 95.8% classification accuracy thanks to the RBF kernel function of SVM and the selected effective features. With this classifier, 93.1% sensitivity, 98.6% specificity, 98.6% precision, 0.92 kappa statistical values and 93.2% area under the ROC curve were obtained.

Keywords

COVID-19, Cough Sounds, Entropy, Machine Learning, MDFA, LZC, SVM.

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