Coronavirus disease (COVID-19) cases analysis using machine-learning applications
By Ameer Sardar Kwekha-Rashid, H. N. Abduljabbar, Bilal S. A. Alhayani. Applied Nanoscience, 2021
301 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Health Information Management, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/f381c53aeb7742e4047d06d84f9e0c4f523231a3
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In Computer Networks and Communications
In Health Information Management
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Machine learning and deep learning approach for medical image analysis: diagnosis to detection (2022, methodology): “Covid 19 25 [53] 2021 ●ML (supervised and unsupervised) ● Data fusion ● Covid-19 Accuracy with supervised ML: 92% Accuracy with unsupervised ML: 7.”