COVID-19 Enfeksiyonunun Nitelik Seçme ile Birleştirilmiş Makine Öğrenmesi Yöntemleriyle Tahmin Edilmesi

COVID-19, 31 Aralık 2019'dan itibaren dünyayı etkisi altına alan ve Mart 2020'de DSÖ tarafından pandemi ilan edilen bir enfeksiyondur. Bu çalışmada, yeni nitelik seçme tabanlı COVID-19 tahmin modelleri oluşturmak ve COVID-19 enfeksiyonunun tahmini için etkili değişkenleri ayırt etmek için minimum fazlalık maksimum önem (mRMR) ve Relief-F nitelik seçiciler ile ayrı ayrı birleştirilmiş Çok Katmanlı Algılayıcı (MLP), Tree Boost (TB), Radyal Temelli Fonksiyon Ağı (RBF), Destek Vektör Makinesi (SVM) ve K-Means Kümeleme (kMC) yöntemleri kullanılmıştır. Veri seti, 20.000 hasta (10.000 pozitif, 10.000 negatif) ile ilgili bilgileri içermektedir ve çeşitli kişisel, semptomatik ve asemptomatik değişkenlerden oluşmaktadır. Modellerin performansını değerlendirmek için doğruluk, duyarlılık ve F1-Skor metrikleri kullanılmıştır ve modellerin genelleme hataları 10 katlı çapraz doğrulama ile değerlendirilmiştir. Sonuçlar, bir hastanın COVID-19 enfeksiyonunu tahmin etmede mRMR’ın ortalama performansının Relief-F’den biraz daha iyi olduğunu göstermektedir. Ek olarak, mRMR’ın, COVID-19 tahmin değişkenlerinin göreceli alaka sırasını bulmada Relief-F algoritmasından daha başarılı olduğu gözlemlenmiştir. mRMR algoritması ateş ve öksürük gibi semptomatik değişkenleri vurgularken, Relief-F algoritması yaş ve ırk gibi asemptomatik değişkenleri öne çıkarmaktadır. Ayrıca, genel olarak MLP’nin COVID-19 enfeksiyonunu tahmin etmede diğer tüm sınıflandırıcılarından daha iyi performans gösterdiği de gözlemlenmiştir.

Anahtar Kelimeler:

Relief-F, mRMR, makine öğrenmesi, tahmin, COVID-19, koronavirüs

Predicting COVID-19 Infection Using Machine Learning Methods Combined with Feature Selection

COVID-19 is an infection that has affected the world since December 31, 2019, and was declared a pandemic by WHO in March 2020. In this study, Multi-Layer Perceptron (MLP), Tree Boost (TB), Radial Basis Function Network (RBF), Support Vector Machine (SVM), and K-Means Clustering (kMC) individually combined with minimum redundancy maximum relevance (mRMR) and Relief-F have been used to construct new feature selection-based COVID-19 prediction models and discern the influential variables for prediction of COVID-19 infection. The dataset has information related to 20.000 patients (i.e., 10.000 positives, 10.000 negatives) and includes several personal, symptomatic, and non-symptomatic variables. The accuracy, recall, and F1-score metrics have been used to assess the models’ performance, whereas the generalization errors of the models were evaluated using 10-fold cross-validation. The results show that the average performance of mRMR is slightly better than Relief-F in predicting the COVID-19 infection of a patient. In addition, mRMR is more successful than the Relief-F algorithm in finding the relative relevance order of the COVID-19 predictors. The mRMR algorithm emphasizes symptomatic variables such as fever and cough, whereas the Relief-F algorithm highlights non-symptomatic variables such as age and race. It has also been observed that, in general, MLP outperforms all other classifiers for predicting the COVID-19 infection.

Keywords:

Relief-F, mRMR, machine learning, prediction, COVID-19, coronavirus,

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