Medical Informatics III

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DOI :10.26650/B/ET07.2023.005.22   IUP :10.26650/B/ET07.2023.005.22    Full Text (PDF)

Prediction of Covid-19 Test Results with Machine Learning Algorithms

Burcu Esin İliş, Emrah Gürlek, Fadime Akdeniz, Nada Misk, Reyhan Şahinbaş, Uygar Aydın, Çiğdem Selçukcan Erol

The COVID-19 epidemic, which has been at the top of the world agenda since the beginning of 2020, continues its existence and impact with the effect of mutations that have emerged at different times. Due to the impact of COVID-19 on individuals and its inter-individual spread, rapid and early diagnosis gains importance. This study it is aimed to predict COVID-19 test results using machine learning algorithms. For this purpose, the COVID-19 dataset of 2,742,596 observations collected between 11.03.2020 and 12.11.2020 of 2020 by the Israeli Ministry of Health was used. Before applying classification algorithms to the data set, six different data sets were obtained by going through different preprocessing processes (deletion of missing data, filling in missing data with logistic regression, and balancing). These six different data sets were obtained as a result of preprocessing; Analysis was carried out by applying six different machine learning algorithms: Support Vector Machines (SVM), Decision TreesC 4.5 Algorithm, Gradient Boosting, Naïve Bayes, Logistic Regression Classifier, and Artificial Neural Networks (ANN). As a result, The highest performance with 80.14% accuracy and 50% no information rate (NIR) was obtained in the balanced (A2) data set by deleting the random data after the deletion of missing data with the decision tree and artificial neural network algorithms.


Keywords: COVID-19, Machine Learning, Classification Algorithms
DOI :10.26650/B/ET07.2023.005.22   IUP :10.26650/B/ET07.2023.005.22    Full Text (PDF)

Makine Öğrenmesi Algoritmaları ile Covıd-19 Test Sonuçlarının Tahmin Edilmesi

Burcu Esin İliş, Emrah Gürlek, Fadime Akdeniz, Nada Misk, Reyhan Şahinbaş, Uygar Aydın, Çiğdem Selçukcan Erol

2020 yılının başından itibaren dünya gündeminin en üst sıralarında yer alan COVID-19 salgını farklı zamanlarda ortaya çıkan mutasyonların da etkisi ile varlığını ve etkisini halen sürdürmektedir. COVID-19’un bireyler üzerindeki etkisi ve bireyler arası yayılımı nedeniyle hızlı ve erken teşhis edilmesi önem kazanmaktadır. Bu çalışmada makine öğrenmesi algoritmaları kullanılarak COVID-19 test sonuçlarının tahmin edilmesi amaçlanmıştır. Bu amaçla İsrail Sağlık Bakanlığı tarafından 2020 yılının 11.03.2020- 12.11.2020 tarihleri arasında toplanan 2.742.596 gözlemden oluşan COVID-19 veri seti kullanılmıştır. Veri setine sınıflandırma algoritmaları uygulamadan önce farklı önişleme süreçlerinden geçirilerek (eksik verilerin silinmesi, lojistik regresyon ile eksik verilerin doldurulması dengeleme) altı ayrı veri seti elde edilmiştir. Önişleme sonucunda elde edilen bu altı farklı veri setine; Destek Vektör Makineleri (DVM), Karar Ağaçları- C 4.5 Algoritması, Gradient Boosting, Naïve Bayes, Lojistik Regresyon Sınıflandırıcı ve Yapay Sinir Ağları (YSA) olmak üzere altı farklı makine öğrenmesi algoritması uygulanarak analiz gerçekleştirilmiştir. Sonuç olarak; %80,14 doğruluk ve %50 no information rate (NIR) ile en yüksek performans karar ağacı ve yapay sinir ağı algoritmaları ile eksik verilerin silinmesi ön işleminden sonra rastgele veri silinerek dengelenmiş (A2) veri setinde elde edilmiştir. 


Keywords: COVID-19, Makine Öğrenmesi, Sınıflandırma Algoritmaları

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