Coronavirus disease (COVID-19) cases analysis using machine-learning applications

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Kwekha-Rashid A. S., Abduljabbar H. N., Alhayani B.

APPLIED NANOSCIENCE, vol.13, no.3, pp.2013-2025, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1007/s13204-021-01868-7
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex
  • Page Numbers: pp.2013-2025
  • Keywords: Artificial intelligence AI, COVID-19, Machine learning, Machine learning tasks, Supervised and un-supervised learning
  • Yıldız Technical University Affiliated: Yes


Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.