Data mining and machine learning techniques for coronavirus (COVID-19) pandemic: A review study

• Main Authors: Ghazi, Alaan, Alisawi, Muthana, Hammood, Layth, Abdullah, Sirwan Saber, Al-Dawoodi, Aras, Ali, Abbas Hussein, Almallah, Ashraf Nabeel, Hazzaa, Nidhal Mohsin, Wahab, Yousif Mohammed, Nawaf, Asmaa Yaseen
• Format: Conference Proceeding
• Language: English
• Subjects: Coronaviruses; COVID-19; Data mining; Machine learning; Public health; Viral diseases; Viruses
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0167882

Data mining and machine learning (ML) methods will be examined in depth in this systematic study. Several medical issues have been addressed as a result of the growing interest in AI development. The danger presented by this virus to global public health means that these applications are inadequate. Data mining and machine learning (ML) methods may be used to automatically identify and diagnose COVID-19, according to this systematic study. Our goals are to get a comprehensive understanding of this dangerous virus, overcome the constraints of data mining and machine learning methods, and make this technology available to the medical community. Three databases, including IEEE Xplore, Web of Science, and Scientific Direct that was Scopus and Clarivate indexing were utilized in our research. Between 2020 and 2022, after obtaining around 1305 papers from these period, precise exclusion criteria and a selection technique were employed. MERS-Covid, SARS-CoV, SARS-CoV-2 including the recent Omicron variant are all CoV family members, and this research will examine the most recent state-of-the-art procedures for each.