The primary goal of this survey is to determine the most widely used data mining approaches and knowledge gaps from published publications. The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Since the threat of pandemics has raised public health concerns, researchers to uncover hidden knowledge have used data extraction techniques. Web of Science, Scopus, and PubMed databases were used to conduct systematic research. Then, to choose good papers, all retrieved publications were reviewed in a stepwise procedure using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. All of the data were examined and summarized using a few different classifications. Out of 300 citations, 50 papers were eligible through a systematic review. The review results showed that the most favorite DM belonged to Natural language processing (22%), and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies predominately apply supervised learning techniques (90%). We found infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline concerning healthcare scopes. The most common software used in the studies was SPSS (22%) and R (20%). Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease. The results revealed valuable research conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. However, most research will need in terms of treatment and disease control.
| Published in | International Journal on Data Science and Technology (Volume 8, Issue 2) |
| DOI | 10.11648/j.ijdst.20220802.11 |
| Page(s) | 36-42 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Public Health, COVID-19, SARS-CoV-2, Machine Learning, Meta-Analyses, Data Mining
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APA Style
Sanjib Ghosh, Lipon Chandra Das. (2022). Using Data Mining Techniques for COVID-19: A Systematic Review. International Journal on Data Science and Technology, 8(2), 36-42. https://doi.org/10.11648/j.ijdst.20220802.11
ACS Style
Sanjib Ghosh; Lipon Chandra Das. Using Data Mining Techniques for COVID-19: A Systematic Review. Int. J. Data Sci. Technol. 2022, 8(2), 36-42. doi: 10.11648/j.ijdst.20220802.11
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Download@article{10.11648/j.ijdst.20220802.11,
author = {Sanjib Ghosh and Lipon Chandra Das},
title = {Using Data Mining Techniques for COVID-19: A Systematic Review},
journal = {International Journal on Data Science and Technology},
volume = {8},
number = {2},
pages = {36-42},
doi = {10.11648/j.ijdst.20220802.11},
url = {https://doi.org/10.11648/j.ijdst.20220802.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20220802.11},
abstract = {The primary goal of this survey is to determine the most widely used data mining approaches and knowledge gaps from published publications. The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Since the threat of pandemics has raised public health concerns, researchers to uncover hidden knowledge have used data extraction techniques. Web of Science, Scopus, and PubMed databases were used to conduct systematic research. Then, to choose good papers, all retrieved publications were reviewed in a stepwise procedure using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. All of the data were examined and summarized using a few different classifications. Out of 300 citations, 50 papers were eligible through a systematic review. The review results showed that the most favorite DM belonged to Natural language processing (22%), and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies predominately apply supervised learning techniques (90%). We found infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline concerning healthcare scopes. The most common software used in the studies was SPSS (22%) and R (20%). Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease. The results revealed valuable research conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. However, most research will need in terms of treatment and disease control.},
year = {2022}
}
TY - JOUR T1 - Using Data Mining Techniques for COVID-19: A Systematic Review AU - Sanjib Ghosh AU - Lipon Chandra Das Y1 - 2022/06/16 PY - 2022 N1 - https://doi.org/10.11648/j.ijdst.20220802.11 DO - 10.11648/j.ijdst.20220802.11 T2 - International Journal on Data Science and Technology JF - International Journal on Data Science and Technology JO - International Journal on Data Science and Technology SP - 36 EP - 42 PB - Science Publishing Group SN - 2472-2235 UR - https://doi.org/10.11648/j.ijdst.20220802.11 AB - The primary goal of this survey is to determine the most widely used data mining approaches and knowledge gaps from published publications. The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Since the threat of pandemics has raised public health concerns, researchers to uncover hidden knowledge have used data extraction techniques. Web of Science, Scopus, and PubMed databases were used to conduct systematic research. Then, to choose good papers, all retrieved publications were reviewed in a stepwise procedure using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. All of the data were examined and summarized using a few different classifications. Out of 300 citations, 50 papers were eligible through a systematic review. The review results showed that the most favorite DM belonged to Natural language processing (22%), and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies predominately apply supervised learning techniques (90%). We found infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline concerning healthcare scopes. The most common software used in the studies was SPSS (22%) and R (20%). Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease. The results revealed valuable research conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. However, most research will need in terms of treatment and disease control. VL - 8 IS - 2 ER -
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