DERİN SİNİR AĞLARI VE YENİDEN ÖRNEKLEME METOTLARI İLE RUTİN KAN TESTLERİNE DAYALI COVID-19 TESPİTİ

Abstract

İlk olarak Aralık 2019’da ortaya çıkan ve dünya çapında bir salgına neden olan Koronavirüs (COVID- 19) hastalığı; akut solunum sendromu SARS-CoV-2’nin neden olduğu viral bir hastalık olarak tanımlanmaktadır. COVID-19 hastalığının tespiti için güncel olan rRT-PCR testi kullanılmaktadır. Bu tes- tin uzun geri dönüş süresi, %15-20 civarında yanlış negatif oranları ve pahalı ekipmanları olması nedeni- yle rutin kan incelemelerinin değerleri ile tespit yöntemi daha hızlı ve daha ucuz bir alternatif olarak değerlendirilebilmektedir. Bu çalışmada, rutin kan testlerinden Derin Sinir Ağları (DSA) kullanılarak COVID-19 tespit edilmeye çalışılmıştır. Kullanılan veri setinde sınıf dengesizliği olduğu için yeniden örnekleme yöntemleriyle sınıf dengesizliği giderilmiş ve kullanılan algoritmaların performansları değer- lendirilmiştir. Yeniden örnekleme yapılırken SMOTE, ADASYN, Geometric SMOTE, Random Under- Sampler, Random OverSampler algoritmaları kullanılmıştır. Kurulan model sonunda 0,985 doğruluk değeri ve 0,99 F1-skoru ile en başarılı sonuç, Random OverSampler algoritması ile alınmıştır. Ayrıca yeni girilecek veriler için tahmin yapabilmek amacıyla, PyQt kullanılarak bir uygulama geliştirilmiştir ve kullanılan niteliklerin modele katkıları SHapley Additive Explanations (SHAP) tekniği ile belirlenmiş ve açıklanmıştır.

Keywords

• Derin Sinir Ağları
• Yeniden Örnekleme
• COVID-19

Covid-19 Detection Based on Routine Blood Tests with Deep Neural Networks and Resampling Methods

Abstract

Coronavirus (COVID-19) disease, which first appeared in December 2019 and caused a worldwide outbreak; is described as a viral disease caused by acute respiratory syndrome SARS-CoV-2. The current RRT-PCR test is used to detect COVID-19 disease. Due to long return time of this test, about 15-20% false-negative rates and expensive equipment, the detection method with the values of routine blood analyses can be considered as a faster and cheaper alternative. In this study, COVID-19 was tried to be detected by using Deep Neural Networks (DNN), one of the routine blood tests. Because there is class imbalance in the used data set, class imbalance has been eliminated by resampling methods and the performance of used algorithms has been evaluated. While resampling, SMOTE, ADASYN, Geometric SMOTE, Random UnderSampler, Random OverSampler algorithms were used. As a result of established model, the most successful result was obtained with the Random OverSampler algorithm, with an accuracy of 0.985 and an F1-score of 0.99. In addition, an application has been developed using PyQt to make predictions for new data to be entered and the contributions of used attributes to the model were determined and explained with the SHapley Additive Explanations (SHAP) technique.

Keywords

• Deep Neural Networks
• Resampling
• COVID-19

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