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. 2023;13(3):2013-2025.
doi: 10.1007/s13204-021-01868-7. Epub 2021 May 21.

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

Ameer Sardar Kwekha-Rashid  1 , Heamn N Abduljabbar  2   3 , Bilal Alhayani  4
Affiliations

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

Ameer Sardar Kwekha-Rashid et al. Appl Nanosci. 2023.

Abstract

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.

Keywords: Artificial intelligence AI; COVID-19; Machine learning; Machine learning tasks; Supervised and un-supervised learning.

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Conflict of interest statement

Conflict of interestThere are no conflicts of interest.

Figures

Fig. 1
Fig. 1
Overview of machine-learning types and tasks
Fig. 2
Fig. 2
Distribution of machine-learning types
Fig. 3
Fig. 3
Distribution of machine-learning tasks
Fig. 4
Fig. 4
Distribution of machine-learning algorithms
See this image and copyright information in PMC

References

    1. Alimadadi A, et al. Artificial intelligence and machine learning to fight COVID-19. Bethesda: American Physiological Society; 2020. - PMC - PubMed
    1. Amar LA, Taha AA, Mohamed MY. Prediction of the final size for COVID-19 epidemic using machine learning: a case study of Egypt. Infect Dis Model. 2020;5:622–634. - PMC - PubMed
    1. Anastasopoulos C, et al. Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: the synergetic effect of an open, clinically embedded software development platform and machine learning. Eur J Radiol. 2020;131:109233. doi: 10.1016/j.ejrad.2020.109233. - DOI - PMC - PubMed
    1. Arvind V, et al. Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19. J Crit Care. 2020;62:25–30. doi: 10.1016/j.jcrc.2020.10.033. - DOI - PMC - PubMed
    1. Assaf D, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435–1443. doi: 10.1007/s11739-020-02475-0. - DOI - PMC - PubMed

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