As reported by the World Health Organization (WHO) in March 2020, COVID-19 is a worldwide epidemic. Since the rapid spread of the epidemic harms humans, the need for methods that enable early diagnosis and treatment has increased. Machine learning (ML) methods can play a vital role in identifying COVID-19 patients. In this study, the classification algorithms of ML methods (CART), Support Vector Machine (SVM-Radial), K Nearest Neighbors (K-NN) and Random Forest are used to determine the best model that diagnoses COVID-19 from the person's complete blood counts (positive/negative). According to the experimental results, the Random Forest algorithm gives the best predictions in the classification of COVID-19 (99.76% of accuracy). Besides, in the classification of Covid-19, it can be recommended to apply meta-learning algorithms as they can give high predictive results.
Birincil Dil | İngilizce |
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Konular | Elektrik Mühendisliği |
Bölüm | Articles |
Yazarlar | |
Yayımlanma Tarihi | 31 Aralık 2020 |
Yayımlandığı Sayı | Yıl 2020 Cilt: 5 Sayı: 2 |