Classification in the change of estimated number of Covid-19 daily cases by using support vector machine and k-nearest neighbor algorithm - Filiz, Enes (Gumushane University, 2022)
Since December 2019, the Covid-19 virus afftected our lives and continues to affect the whole world significantly. The investigistion of the indicators of the Covid-19 virus and vaccination studies are of great interest to overcome the Covid-19 pandemic based on the World health organization recommendations. In this context, many scientific studies have revealed valuable information for the future of the virus. In this study, estimation of the cOvid-19 cases and Classification of changes in the daily number of cases in Turkey was carried out by using support vector machine and k-nearest neighbor algorithms. The indicators that play a critical role in the estimation of the daily patient number classification have been determined as "positivity rate", "fillation rate", "workplace mobility" and "mobility in parks". It has been observed that the k-nearest neighbor algorithm (84.7%) is the most successful algorithm in the estimation of the daily number of cases when considering the highlighted features. © 2022, Gumushane University. All rights reserved.
Türkiye Covid-19 günlük hasta sayısındaki değişimin sınıflandırılmasına yönelik tahmininin destek vektör makineleri ve k-en yakın komşu algoritmaları ile gerçekleştirilmesi
Keywords: Classification; Covid-19; Daily case; Feature selection; Machine learning