Classification of the death ratio of COVID-19 Pandemic using Machine Learning Techniques

Abstract

Since the COVID-19 pandemic has appeared, many epidemiological models are developed around the world to estimate the number of infected individuals and the death ratio of the COVID-19 outbreak. There are several models developed on COVID-19 by using machine learning techniques. However, studies that considered feature selection in detail are very limited. Therefore, the aim of this study is to (i) investigate the independent and interactive effects of a diverse set of features and (ii) find the algorithms that are significant for classifying the death ratio of the COVID-19 outbreak. It was found that logistic regression and decision tree (C4.5, Random Forests, and REPTree) are the most suitable algorithms. A diverse set of features obtained by feature selection methods are the number of new tests per thousand, new cases per million, hospital patients per million, and weekly hospital admissions per million. The importance of this study is that a high rate of classification was obtained with a few features. This study showed that only the most relevant features should be considered in classification and the use of all variables in classification is not necessary.

Keywords

• Classification
• Machine learning
• Decision tree
• COVID-19
• Feature selection

Makine Öğrenimi Teknikleri kullanılarak COVID-19 Pandemisinin ölüm oranının sınıflandırılması

Abstract

COVID-19 pandemisi ortaya çıktığından beri, enfekte olmuş bireylerin sayısını ve COVID-19 salgınının ölüm oranını tahmin etmek için dünya çapında birçok epidemiyolojik model geliştirilmiştir. CoVID-19 üzerinde makine öğrenimi teknikleri kullanılarak geliştirilmiş birkaç model bulunmaktadır. Ancak öznitelik seçimini ayrıntılı olarak ele alan çalışmalar oldukça sınırlıdır. Bu nedenle, bu çalışmanın amacı (i) çeşitli özelliklerin bağımsız ve etkileşimli etkilerini araştırmak ve (ii) COVID-19 salgınının ölüm oranını sınıflandırmak için önemli olan algoritmaları bulmaktır. Lojistik regresyon ve karar ağacının (C4.5, Random Forests ve REPTree) en uygun algoritmalar olduğu bulunmuştur. Öznitelik seçme yöntemleriyle elde edilen çeşitli öznitelikler, binde yeni test sayısı, milyonda yeni vaka, milyonda hastane hasta sayısı ve milyonda haftalık hastane kabulüdür. Bu çalışmanın önemi, birkaç özellik ile yüksek oranda sınıflandırma elde edilmiş olmasıdır. Bu çalışma, sınıflandırmada sadece en ilgili özelliklerin dikkate alınması gerektiğini ve sınıflandırmada tüm değişkenlerin kullanılmasının gerekli olmadığını göstermiştir.

Keywords

• Sınıflandırma
• Makine öğrenmesi
• Karar ağaçları
• COVID-19
• Öznitelik seçimi

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