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Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

Background and Objective: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a diff...

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Main Authors:	Chiroma, Haruna, Ezugwu, Absalom E., Jauro, Fatsuma, Al-Garadi, Mohammed A., Abdullahi, Idris N., Shuib, Liyana
Format:	Article
Published:	PeerJ 2020
Subjects:	R Medicine (General)
Online Access:	http://eprints.um.edu.my/36254/
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spelling	my.um.eprints.362542023-10-24T03:08:11Z http://eprints.um.edu.my/36254/ Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks Chiroma, Haruna Ezugwu, Absalom E. Jauro, Fatsuma Al-Garadi, Mohammed A. Abdullahi, Idris N. Shuib, Liyana R Medicine (General) Background and Objective: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. Methods: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control. PeerJ 2020-11 Article PeerReviewed Chiroma, Haruna and Ezugwu, Absalom E. and Jauro, Fatsuma and Al-Garadi, Mohammed A. and Abdullahi, Idris N. and Shuib, Liyana (2020) Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks. PeerJ Computer Science. ISSN 2376-5992, DOI https://doi.org/10.7717/peerj-cs.313 <https://doi.org/10.7717/peerj-cs.313>. 10.7717/peerj-cs.313
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	R Medicine (General)
spellingShingle	R Medicine (General) Chiroma, Haruna Ezugwu, Absalom E. Jauro, Fatsuma Al-Garadi, Mohammed A. Abdullahi, Idris N. Shuib, Liyana Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
description	Background and Objective: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. Methods: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.
format	Article
author	Chiroma, Haruna Ezugwu, Absalom E. Jauro, Fatsuma Al-Garadi, Mohammed A. Abdullahi, Idris N. Shuib, Liyana
author_facet	Chiroma, Haruna Ezugwu, Absalom E. Jauro, Fatsuma Al-Garadi, Mohammed A. Abdullahi, Idris N. Shuib, Liyana
author_sort	Chiroma, Haruna
title	Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
title_short	Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
title_full	Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
title_fullStr	Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
title_full_unstemmed	Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks
title_sort	early survey with bibliometric analysis on machine learning approaches in controlling covid-19 outbreaks
publisher	PeerJ
publishDate	2020
url	http://eprints.um.edu.my/36254/
_version_	1781704493114589184
score	13.211869

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks

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