VARYASYONEL MOD AYRIŞTIRMASIYLA ÖKSÜRÜK SESLERİNDEN KOVİD-19 TESPİTİ

Dünya Sağlık Örgütü'ne göre öksürük, küresel bir pandemi olarak ilan edilen COVID-19 hastalığının en belirgin semptomlarından biridir. Bu semptom, tıbbi muayene için kliniğe gelen COVID-19 hastası kişilerin %68 ila %83'ünde görülür. Bu nedenle pandemi sırasında, öksürük COVID-19'un teşhis edilmesinde ve hastaların sağlıklılardan ayırt edilmesinde önemli bir rol oynamaktadır. Bu çalışma, COVID-19 pozitif kişilerin öksürük seslerini COVID-19 negatif olanlardan ayırt etmeyi ve böylece COVID-19 tanısına destek sağlamayı amaçlamaktadır. Bu amaçla COVID-19 ve COVID-19 değil olarak etiketlenmiş öksürük seslerini içeren “Virufy” veri seti dahil edilmiştir. Verileri dengelemek için ADASYN tekniği kullanıldıktan sonra, Varyasyonel Mod Ayrıştırma (VMD) yöntemi kullanılarak her bir ses için bağımsız modlar elde edilmiş ve her moddan çeşitli öznitelikler çıkarılmıştır. Daha sonra, ReliefF algoritması ile en etkili olan özellikler seçilmiştir. Ardından, sınıflandırma yoluyla COVID-19 ve COVID-19 olmayan öksürük seslerini tanımlamak için topluluk makine öğrenme yöntemleri (Rastgele Orman, Gradyan Artırma Makineleri ve Adaboost) tercih edilmiştir. Sonuç olarak en iyi performans Gradyan Artırma Makineleri ile %94,19 doğruluk, %87,67 duyarlılık, %100 özgüllük, %100 kesinlik, %93,43 F-skor, 0,88 kappa ve %93,87 ROC eğrisi altında kalan alan olarak elde edilmiştir.

Anahtar Kelimeler:

ADASYN, Öksürük Sesleri, COVID-19, Topluluk Makine Öğrenmesi, ReliefF, Varyasyonel Mod Ayrıştırma (VMD)

COVID-19 DETECTION USING VARIATIONAL MODE DECOMPOSITION OF COUGH SOUNDS

According to the World Health Organization, cough is one of the most prominent symptoms of the COVID-19 disease declared as a global pandemic. The symptom is seen in 68% to 83% of people with COVID-19 who come to the clinic for medical examination. Therefore, during the pandemic, cough plays an important role in diagnosing of COVID-19 and distinguishing patients from healthy individuals. This study aims to distinguish the cough sounds of COVID-19 positive people from those of COVID-19 negative, thus providing automatic detection and support for the diagnosis of COVID-19. For this aim, “Virufy” dataset containing cough sounds labeled as COVID-19 and Non COVID-19 was included. After using the ADASYN technique to balance the data, independent modes were obtained for each sound by utilizing the Variational Mode Decomposition (VMD) method and various features were extracted from every mode. Afterward, the most effective features were selected by ReliefF algorithm. Following, ensemble machine learning methods, namely Random Forest, Gradient Boosting Machine and Adaboost were prepared to identify cough sounds as COVID-19 and Non COVID-19 through classification. As a result, the best performance was obtained with the Gradient Boosting Machine as 94.19% accuracy, 87.67% sensitivity, 100% specificity, 100% precision, 93.43% F-score, 0.88 kappa and 93.87% area under the ROC curve.

Keywords:

ADASYN, Cough Sound, COVID-19, Ensemble Machine Learning, ReliefF, Variational Mode Decomposition (VMD),

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