Nedim MUZOĞLU, Melike Kaya KARASLAN, Ahmet Mesrur HALEFOĞLU, Bekir Sıddık Binboğa YARMAN

Boruta Öznitelik Seçimi Algoritması ve Derin Öğrenme Yöntemleri Kullanılarak Covid-19 Hastalığının Prognozunun Tahmini

Covid-19 pandemisi nedeniyle milyonlarca insan hayatını kaybetmiş ve birçok ülkede yetersiz sağlık sistemleri hizmet veremez hale gelmiştir. Covid-19 hastalarının yoğun bakım ve ventilasyon ihtiyaçlarının belirlenerek hastalığın prognozu hakkında tahminlerde bulunulması, hastanın sağlık durumu ve sağlık sistemlerinin etkin kullanımı açısından önemlidir. Bu amaçla oluşturulan Covid-19 akciğer bilgisayarlı tomografi (BT) bulguları veri seti buzlu cam opasitesi, konsolidasyon, kaldırım taşı paterni, konsodilasyon ve buzlu cam, nodül ve buzlu cam sınıflarını içermektedir. Bu çalışmada önerilen yaklaşım dört adımdan oluşmaktadır. Birinci adımda VGG-16 modeli akciğer BT bulguları veri seti ile eğitilmiştir. İkinci adımda elde edilen en ayırt edici öznitelikler BORUTA algoritması kullanılarak seçilmiştir. Üçüncü adımda sıralama yöntemiyle her görüntü için en değerli ilk 200, 300 ve 400 öznitelikler elde edilmiştir. Son adımda ise Destek Vektör Makineleri ve Lineer Diskriminant Analizi ile bu özellikler sınıflandırılmıştır. Akciğer BT bulguları veri seti için elde edilen genel doğruluk %97,02'dir. Derin Öğrenme yöntemleri ile Covid-19 hastalık prognozunu tahmin etmek için oluşturulan veri seti kullanılarak elde edilen bu başarılı sonuç, viral pnömoni türlerinin akciğer BT bulgularının sınıflandırılmasında çok önemli bir yeniliktir.

Prediction of the Prognosis of Covid-19 Disease Using Deep Learning Methods and Boruta Feature Selection Algorithm

Millions of people have lost their lives due to the Covid 19 pandemic, and inadequate health systems have been overwhelmed in many countries. Determining the intensive care and ventilation needs of Covid-19 patients and thus making predictions about the prognosis of the disease is crucial in terms of the patient's health status and the effective use of health systems. The Covid-19 chest computed tomography (CT) findings dataset created for this purpose consists of ground-glass opacity (GGO), consolidation, crazy paving pattern (CPP), consolidation and ground glass (GGOC), nodule and ground glass classes (GGON). The approach proposed in this study consists of four steps. The VGG16 model was trained with the chest CT findings dataset in the first step. The most discriminative features obtained in the second step were selected using the BORUTA algorithm. In the third step, the most valuable top 200, 300 and 400 features for each image were obtained by ranking method. In the last step these features were classified with Support Vector Machines and Linear Discriminant Analysis. The overall accuracy obtained for the chest CT findings dataset is 97.02%. This successful result, obtained using the dataset to predict Covid 19 disease prognosis with Deep Learning methods, is a crucial innovation in the classification of chest CT findings in viral types of pneumonia.

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1 -https://covid19.who.int/,(04.05.2022)