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

• Published in: PeerJ. Computer science 2020-11, Vol.6, p.e313, Article e313
• Main Authors: Chiroma, Haruna, Ezugwu, Absalom E, Jauro, Fatsuma, Al-Garadi, Mohammed A, Abdullahi, Idris N, Shuib, Liyana
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Bibliometrics; Bioinformatics; Cable television broadcasting industry; CAT scans; Chest; China; Citation analysis; Computed tomography; Coronaviruses; COVID-19; Data Mining and Machine Learning; Datasets; Diagnostic software; Diagnostic systems; Disease control; Disease prevention; Disease transmission; Earthquakes; Epidemics; Health aspects; Infections; Iran; Keywords; Literature reviews; Machine learning; Medical imaging; Medical research; Mortality; Neural networks; Outbreaks; Pandemics; Peer review; Severe acute respiratory syndrome coronavirus 2; Surveys; Systematic review; Taxonomy; United States; Viral diseases; Viral infections
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.313

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. 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. 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. 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 bibli