Ünal Halit ÖZDEN, Kubilay ERİŞLİK

Kan Tahlili Sonuçlarına Göre Diskriminant Analizi ve Yapay Sinir Ağları ile Covid-19 Teşhisinin Belirlenmesi

31 Aralık 2019’da tipi bilinmeyen 27 zatürre vakası Çin’in Hubei eyaleti, Wuhan şehrinde tespit edilmiştir. 30 Ocak 2020’de WHO, Çin’deki COVID-19 salgınını, savunmasız sağlık sistemlerine sahip ülkeler için yüksek risk oluşturan Uluslararası Öneme Sahip Halk Sağlığı Acil Durumu olarak ilan etmiştir. COVID-19 hastalığının teşhisinden üst solunum yollarından ve alt solunum yollarından alınan örnekler kullanılmaktadır. Bu çalışmada RT-PCR testine alternatif olabilecek bir yöntem sunabilmek, RT-PCR testinin %60 doğru teşhis oranından daha güvenilir bir teşhis yöntemi oluşturmak ve üst solunum yollarından süprüntü örneği almadan sadece kan tahlili ile COVID-19’un teşhis edip edilemeyeceğinin tespit edilmesi amaçlanmıştır. Çalışmada São Paulo, Brezilya’daki Israelita Albert Einstein Hastanesinde RT-PCR testi yaptırmış 159 hastanın Hemotokrit, Hemoglobin, Ortalama Trombosit Hacmi, Kırmızı Kan Hücresi Sayısı ve RT-PCR testi sonuçlarından yararlanılmıştır. Hastaların COVID-19 teşhisinde diskriminant analizi ve yapay sinir ağları modelleri kullanılmıştır. Çalışmanın sonucunda diskriminant analizi %89,9, yapay sinir ağları ise %93,7 oranında başarılı olmuşlardır. Yapay sinir ağları ile oluşturulan modelin pozitif hastaları tahmin etmede diskriminant analizine göre daha başarılı olduğu tespit edilmiştir.

Anahtar Kelimeler:

COVID-19, Diskrminant Analizi, Hastalık Teşhisi, RT-PCR, Sınıflandırma, Yapay Sinir Ağları

Determination of COVID-19 Diagnosis with Discriminant Analysis and Artificial Neural Networks According to Blood Test Results

On December 31, 2019, 27 pneumonia cases of unknown type were detected in China's Wuhan city, Hubei province. On January 30, 2020, World Health Organization (WHO) declared the Coronavirus Disease (COVID)-19 outbreak in China as a Public Health Emergency of International Concern, posing a high risk for countries with vulnerable health systems. Samples taken from the upper respiratory tract and lower respiratory tract are used in the diagnosis of COVID-19. In this study, it was aimed to provide a method that could be an alternative to Reverse Transcription-Polymerase Chain Reaction RT-PCR test, to create a more reliable diagnostic method than the 60% accurate diagnosis rate of RT-PCR test, and to determine whether COVID-19 could be diagnosed by only blood analysis without taking a swab sample from the upper respiratory tract. Hematocrit, hemoglobin, mean platelet volume, red blood cell count and RT-PCR test results of 159 patients who had RT-PCR test at Hospital Sírio-Libanês in São Paulo, Brazil were used in the study. Discriminant analysis and artificial neural network models were used in the diagnosis of COVID-19 of the patients. As a result of the study, discriminant analysis was 89.9% successful, and artificial neural networks were 93.7% successful. According to this result, it was determined that the model created with artificial neural networks is more successful than discriminant analysis in predicting positive patients.

Keywords:

Artificial Neural Networks, Classification, COVID-19, Discriminant Analysis, Disease Diagnosis, RT-PCR,

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