Kwekha-Rashid Ameer Sardar, Abduljabbar Heamn N, Alhayani Bilal
Business Information Technology, College of Administration and Economics, University of Sulaimani, Sulaimaniya, Iraq.
College of Education, Physics Department, Salahaddin University, Shaqlawa, Iraq.
Appl Nanosci. 2023;13(3):2013-2025. doi: 10.1007/s13204-021-01868-7. Epub 2021 May 21.
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.
如今，全世界都在关注冠状病毒病，这意味着即使是这种大流行疾病也并非独一无二。本研究的目的是检测机器学习应用和算法在调查及处理新冠疫情的各种目的中所起的作用。通过在科学Direct、施普林格、Hindawi和MDPI上搜索，以新冠病毒、机器学习、监督学习和无监督学习为关键词，回顾2020年期间发表的与该主题相关的研究。总共获得了16306篇文章，但经过筛选后，本研究仅纳入了其中14项研究。我们的研究结果表明，机器学习在新冠疫情调查、预测和辨别方面可以发挥重要作用。总之，机器学习可参与医疗服务提供者评估和分流新冠病例的项目与计划。监督学习的测试准确率为92.9%，比其他无监督学习算法表现更好。未来，循环监督学习可用于实现更高的准确率。
