Coronavirus disease (COVID-19) cases analysis using machine-learning applications - Kwekha-Rashid, Ameer Sardar; Abduljabbar, Heamn N.; Alhayani, Bilal (SPRINGER HEIDELBERG, 2021)
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: COVID-19; Artificial intelligence AI; Machine learning; Machine learning tasks; Supervised and un-supervised learning; PREDICTION; ALGORITHMS; Science & Technology - Other Topics