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Association Rules Extraction From the Coronavirus Disease 2019: Attributes on Morbidity and Mortality
International Journal of Healthcare Information Systems and Informatics ; 17(1):2023/10/01 00:00:00.000, 2022.
Article in English | ProQuest Central | ID: covidwho-2227728
ABSTRACT
This research was aimed to extract association rules on the morbidity and mortality of corona virus disease 2019 (COVID-19). The dataset has four attributes that determine morbidity and mortality;including Confirmed Cases, New Cases, Deaths, and New Deaths. The dataset was obtained as of 2nd April, 2020 from the WHO website and converted to transaction format. The Apriori algorithm was then deployed to extract association rules on these attributes. Six rules were extracted Rule 1. {Deaths, NewDeaths}=>{NewCases}, Rule 2. {ConfCases, NewDeaths}=>{NewCases}, Rule 3. {ConfCases, Deaths}=>{NewCases}, Rule 4. {Deaths, NewCases}=>{NewDeaths}, Rule 5. {ConfCases, Deaths}=>{NewDeaths}, Rule 6. {ConfCases, NewCases}=>{NewDeaths}, with confidence 0.96, 0.96, 0.86, 0.66, 0.59, 0.51 respectively. These rules provide useful information that is vital on how to curtail further spread and deaths from the virus, both in areas where the pandemic is already ravaging and in areas yet to experience the outbreak.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: International Journal of Healthcare Information Systems and Informatics Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: International Journal of Healthcare Information Systems and Informatics Year: 2022 Document Type: Article