Covid-19'da Pozitif Vaka Oranı Tahmini İçin Aşırı Öğrenme Makinesi Algoritmaları: Karşılaştırmalı Bir Çalışma

Year 2023, Volume: 11 Issue: 1, 170 - 188, 31.01.2023

Salih Berkan Aydemir , Funda Kutlu Onay

https://doi.org/10.29130/dubited.999953

Abstract

Dünya tarihinde bugüne kadar çeşitli pandemiler meydana gelmiştir. 2019 yılının sonunda ortaya çıkan Covid-19 salgını son zamanlarda literatürde güncel bir konu haline geldi. Bu çalışmada, aşırı öğrenme algoritmaları, en fazla pozitif vakaların görüldüğü ülkeler olan Hindistan, Türkiye, İtalya, ABD ve İngiltere için pozitif oranı tahmin etmeye yönelik karşılaştırmalı bir çalışma olarak sunulmaktadır. F-testi öznitelik seçme yöntemi ile öğrenme aşamasında kullanılacak öznitelikler belirlenir. Her bir aşırı öğrenme yaklaşımı ve her bir ülke için hata ortalama karekökü değerlendirme kriterleri ile sonuçlar elde edilir. Buna göre, radyal tabanlı çekirdek fonksiyonu en iyi tahmin sonuçlarını üretirken, doğrusal çekirdek fonksiyonu en yüksek RMSE'ye sahiptir. Buna göre Hindistan için en düşük RMSE değeri radyal tabanlı çekirdek fonksiyonu tabanlı ELM ile 4.17E-03 olarak elde edilmiştir. Ayrıca Türkiye verileri çok fazla aykırı değer içerdiğinden doğrusal çekirdek yönteminde ülkeler arasında en yüksek RMSE değerine (0.015 - 0.035) sahiptir.

Keywords

Aşırı Öğrenme Makinaları, Covid-19, Tahmin, Öznitelik Seçimi

Extreme Learning Machine Algorithms for Prediction of Positive Rate in Covid-19: A Comparative Study

Abstract

Various pandemics have been recorded in world history until today. The Covid-19 outbreak, which emerged at the end of 2019, has recently been a hot topic in the literature. In this work, extreme learning algorithms are presented as a comparative study for predicting the positive rate for the countries: India, Turkey, Italy, USA and UK. The features to be used in the learning phase are determined with the F-test feature selection method. For each extreme learning approach, results are obtained for each country with the root mean square error evaluation criteria. Accordingly, the radial basis kernel function produces the best estimation results, while the linear kernel function has the highest RMSE. Accordingly, the lowest RMSE value has been obtained for India as 4.17E-03 with the radial basis kernel function based ELM. Also, since Turkey's data contains too many outliers, it has the highest RMSE value (0.015 - 0.035) in linear kernel method among the countries.

Keywords

Extreme Learning Machines, Prediction, Feature Selection, Aşırı Öğrenme Makinaları, Covid-19, Tahmin, Öznitelik Seçimi

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