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. 2021;11(4):759-771.
doi: 10.1007/s12553-021-00553-7. Epub 2021 May 7.

Using data mining techniques to fight and control epidemics: A scoping review

Reza Safdari  1 , Sorayya Rezayi  2 , Soheila Saeedi  2   3 , Mozhgan Tanhapour  2 , Marsa Gholamzadeh  1
Affiliations

Using data mining techniques to fight and control epidemics: A scoping review

Reza Safdari et al. Health Technol (Berl). 2021.

Abstract

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping 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 show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Keywords: COVID-19; Data mining; Pandemics; Review.

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Conflict of interest statement

Conflict of interestThe authors declare that they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
The PRISMA diagram for the identification, screening, and eligibility of studies
Fig. 2
Fig. 2
The distribution of papers by their month of publications in 2020
Fig. 3
Fig. 3
Word cloud of most applied words in reviewed articles
Fig. 4
Fig. 4
The distribution of papers based on countries
Fig. 5
Fig. 5
Distribution of employed DM techniques regarding main approaches
Fig. 5
Fig. 5
The frequency of main health disciplines in reviewed articles
See this image and copyright information in PMC

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