Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması

Year 2022, Volume: 5 Issue: 2, 127 - 136, 21.09.2022

Ebru Efeoğlu

https://doi.org/10.38016/jista.1082310

Abstract

Covid-19 virüsü 2019 yılında ortaya çıktı ve kısa bir sürede tüm dünyaya yayıldı. Milyonlarca insanın enfekte olmasına ve yüz binlerce insanın ölümüne neden oldu. Her geçen gün vaka sayısı artmakta ve virüsün yeni varyantlar meydana gelmektedir. Bu hastalığa sahip kişileri tespit etmek için Polimeraz Zincir Reaksiyonu (PCR) testleri uygulanmaktadır. Hastalığı tespit edilen kişilerin durumlarının incelenmesi yoğun bakım ve ölüm oranlarının önceden tespiti oldukça önemlidir. Bu çalışmada Covid-19 hastalarından ölüm oranlarının tespitinde özellik çıkarımı yöntemi olarak Temel Bileşen Analizi (PCA) kullanılmış ve yöntemin başarılı sonuçları en popüler makine öğrenmesi teknikleri ile gösterilmiştir. Çalışmada kullanılan makine öğrenmesi teknikleri K-En Yakın Komşu (KNN), Doğrusal Ayrımcılık Analizi (LDA), Extra Ağaçlar, Random Tree, Rep Tree ve Naive Bayes algoritmalarıdır. Bu tekniklerin performans değerlendirmesinde Doğruluk, Kesinlik, Duyarlılık, Rms, F-skoru değerleri hesaplanmıştır. Ayrıca ROC Eğrileri ve Karışıklık matrisleri incelenerek sonuçlar karşılaştırılmıştır. Sonuç olarak, en iyi performansın Temel bileşenler analizi uygulandıktan sonra Doğrusal Ayrım Analizi (PCA+LDA) kullanımı ile elde edildiği görülmüştür. PCA+LDA uygulaması ile %96,39 Doğruluk oranı elde edilmiştir. Makalede ayrıca özellik çıkarımının kullanılmasıyla Covid-19 virüsünden Zatürre, Şeker, KOAH ve Astım hastalarının, hamile, yaşlı ve entrube insanların daha çok etkilendiği ve ölüm riskinin daha yüksek olduğu ortaya çıkmıştır. Virüsün varyantlarının ölümcüllüğünün incelenmesi, riskli hastaların tedavisi, ölüm riski bulunan hastaların izolasyonu için gereken önlemlerin alınması ve hastane kapasite planlamasının iyileştirilmesi açısından bu çalışma önem arz etmektedir.

Keywords

Covid-19, Performans Analizi, Sınıflandırma Algoritmaları, Temel Bileşen Analizi

Using Principal Component Analysis to Identify Mortality Rates of Covid-19 Patients and Patients at High Risk of Death

Abstract

The Covid-19 virus emerged in 2019 and spread all over the world in a short time. It caused millions of people to be infected and hundreds of thousands to die. The number of cases is increasing day by day and new variants of the virus are emerging. Polymerase Chain Reaction (PCR) tests are used to detect people with this disease. It is very important to examine the conditions of the people with the disease and to determine the intensive care and mortality rates in advance. In this study, Principal Component Analysis (PCA) was used as a feature extraction method to determine mortality rates from Covid-19 patients, and the successful results of the method were demonstrated with the most popular machine learning techniques. Machine learning techniques used in the study are K-Nearest Neighbor (KNN), Linear Discrimination Analysis (LDA), Extra Trees, Random Tree, Rep Tree and Naive Bayes algorithms. In the performance evaluation of these techniques, Accuracy, Precision, Sensitivity, Rms, F-score values were calculated. In addition, ROC Curves and Confusion matrices were examined and the results were compared. As a result, it was seen that the best performance was obtained with the use of Linear Discrimination Analysis (PCA+LDA) after applying Principal component analysis. With the PCA+LDA application, an accuracy rate of 96.39% was obtained. In the article, it has also been revealed that Pneumonia, Diabetes, COPD and Asthma patients, Pregnant, Elderly and Intubated people are more affected and the risk of death is higher from the Covid- 19 virus by using feature extraction. This study is important in terms of examining the lethality of virus variants, taking the necessary precautions for the treatment of risky patients isolation of patients at risk of death, and improving hospital capacity planning.

Keywords

Covid-19, Performance Analysis, Classifications, Principal Component Analysis

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