A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19)

Abstract

The global pandemic of new coronaviruses (COVID-19) has infected many people around the world and became a worldwide concern since this disease caused illness and deaths. The vaccine and drugs are not scientifically established, but patients are recovering with antibiotic drugs, antiviral medicine, chloroquine, and vitamin C. Now it is obvious to the world that a quicker and faster solution is needed for monitoring and combating the further spread of COVID-19 worldwide, using non-clinical techniques, for example, data mining tools, enhanced intelligence, and other artificial intelligence technologies. In this paper, association rule mining is developing for the frequent itemsets discovery in COVID-19 datasets, and the extraction of effective association relations between them. This is done by demonstrates the analysis of the Coronavirus dataset by using the Apriori_Association_Rules algorithm. It involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. The major contribution of this study employment determines the effectiveness of the Apriori_Association_Rules algorithm towards a classification of medical reports. The experimental results provide evidence of the Apriori_Association_Rules algorithm regarding the execution time, memory consumption, and several associated rules that reflect its potential applications to different contexts. Therefore, the Apriori_Association_Rules algorithm will be very useful in healthcare fields to demonstrate the latest developments in medical studies fighting COVID-19.

Association rule mining for prediction of COVID-19

“…Thus, for this example, the following association rules are developed: S5→(S2,S1); (S1,S5)→S2 and (S2,S5)→S1. It has been noticed that the apriori algorithm of association rule mining has already been successfully deployed for prediction/diagnosis of heart diseases (Said et al, 2015;Domadiya & Rao, 2018;Jamsheela, 2021), dengue (Jahangir et al, 2018), brain tumor (Sengupta et al, 2013), chronic kidney disease (Alaiad et al, 2020), infectious diseases (Brossette et al, 1998), pandemic diseases (Burvin & Dhanalakshmi, 2018;Aiswarya et al, 2020), COVID-19 (Çelik, 2020;Shawkat et al, 2021;Tandan et al, 2021), pediatric primary care (Downs & Wallace, 2000), treatment of patients in an emergency department (Sarıyer & Taşar, 2020) etc. In this paper, based on a huge dataset of COVID-19 patients and using the FP growth algorithm of association rule mining, an attempt is put forward to discover COVID-19 symptom patterns and rules which would support the initial identification of severe COVID-19 cases for early treatment and isolation.…”

Description of health patterns of fully vaccinated older adults hospitalized due to COVID-19 in Brazil through association rules

“…Advanced data mining techniques, such as Association Rules Mining (ARM), have been used in the recognition of patterns of these conditions in populations during the COVID-19 pandemic (Shawkat et al, 2021). Data mining can be described as an intelligent method that allows the identification of useful and understandable patterns and relationships of items in a database, which has ARM as one of its oldest fields in recognizing these patterns (Williams, 2011).…”