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

• aşırı öğrenme makinaları
• covid-19
• tahmin
• öznitelik seçimi
• extreme learning machines
• prediction
• feature selection
• aşırı öğrenme makinaları
• covid-19
• tahmin
• öznitelik seçimi