A PROPOSED MODEL CAN CLASSIFY THE COVID-19 PANDEMIC BASED ON THE LABORATORY TEST RESULTS

Year 2020, Volume: 5 Issue: 2, 60 - 63, 31.12.2020

Şeyma Yaşar , Cemil Çolak

Abstract

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.

Keywords

COVID-19, Machine Learning, CART, SVM, K-NN, Random Forest

References

• [1] Z. Y. Zu, M. D. Jiang, P. P. Xu, W. Chen, Q. Q. Ni, G. M. Lu, et al., "Coronavirus disease 2019 (COVID-19): a perspective from China," Radiology, p. 200490, 2020.
• [2] R. Mitchell, J. Michalski, and T. Carbonell, An artificial intelligence approach: Springer, 2013.
• [3] A. Rajkomar, J. Dean, and I. Kohane, "Machine learning in medicine," New England Journal of Medicine, vol. 380, pp. 1347-1358, 2019.
• [4] R. Bost, R. A. Popa, S. Tu, and S. Goldwasser, "Machine learning classification over encrypted data," in NDSS, 2015, p. 4325.
• [5] D. Dietrich, B. Heller, and B. Yang, "Data science & big data analytics: Discovering," Analyzing, Visualizing and Presenting Data, 2015.
• [6] E. GÜLDOĞAN, A. K. ARSLAN, and J. YAĞMUR, "Çeşitli Çekirdek Fonksiyonları ile Oluşturulan Destek Vektör Makinesi Modellerinin Performanslarının İncelenmesi: Bir Klinik Uygulama," Firat Tip Dergisi, vol. 22, 2017.
• [7] D. KILINÇ, E. BORANDAĞ, F. YÜCALAR, V. TUNALI, M. ŞİMŞEK, and A. ÖZÇİFT, "KNN algoritması ve r dili ile metin madenciliği kullanılarak bilimsel makale tasnifi," 2016.
• [8] T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, "How many trees in a random forest?," in International workshop on machine learning and data mining in pattern recognition, 2012, pp. 154-168.
• [9] C.-F. Chien and L.-F. Chen, "Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry," Expert Systems with applications, vol. 34, pp. 280-290, 2008.
• [10] J. Han and M. Kamber, "Data Mining Concepts and Techniques, Morgan Kaufmann Publishers," San Francisco, CA, pp. 335-391, 2001.
• [11] W. Y. Loh, "Classification and regression trees," Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 1, pp. 14-23, 2011.
• [12] Q. Song, W. Hu, and W. Xie, "Robust support vector machine with bullet hole image classification," IEEE transactions on systems, man, and cybernetics, part C (applications and reviews), vol. 32, pp. 440-448, 2002.
• [13] J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques: Elsevier, 2011.
• [14] L. Breiman, "Random forests," Machine learning, vol. 45, pp. 5-32, 2001.
• [15] K. J. Archer and R. V. Kimes, "Empirical characterization of random forest variable importance measures," Computational statistics & data analysis, vol. 52, pp. 2249-2260, 2008.
• [16] H. A. Rothan and S. N. Byrareddy, "The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak," Journal of autoimmunity, p. 102433, 2020.
• [17] S. Chatterjee, F. Saad, C. Sarasaen, S. Ghosh, R. Khatun, P. Radeva, et al., "Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images," arXiv preprint arXiv:2006.02570, 2020.
• [18] N. Khadem and A. Mohammadi, "Application of Deep Learning Technique to Manage COVID-19 in Routine Clinical Practice Using CT Images: Results of 10 Convolutional Neural Networks."
• [19] T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim, and U. R. Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images," Computers in Biology and Medicine, p. 103792, 2020.
• [20] A. Banerjee, S. Ray, B. Vorselaars, J. Kitson, M. Mamalakis, S. Weeks, et al., "Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population," International immunopharmacology, vol. 86, p. 106705, 2020.
• [21] M. Yavaş, A. Güran, and M. Uysal, "Covid-19 Veri Kümesinin SMOTE Tabanlı Örnekleme Yöntemi Uygulanarak Sınıflandırılması," Avrupa Bilim ve Teknoloji Dergisi, pp. 258-264.