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An Assessment of the Missing Data Imputation Techniques for COVID-19 Data
3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2021 ; 915:701-706, 2022.
Article in English | Scopus | ID: covidwho-2059754
ABSTRACT
In medical domain, the accuracy of the data supplied is critical. Missing values, on the other hand, are a typical occurrence in this sector for a variety of reasons. Most current science concentrates on establishing novel data imputation procedures, but more research on conducting a comprehensive review of existing algorithms is highly desired. Authors have evaluated the performance of four mostly adopted data imputation techniques, i.e., MICE, EM, mean, and KNN on a real-world dataset of COVID-19. KNN is an imputation approach that, according to the findings of the studies, is expected to be a good fit for dealing with missing data in the healthcare industry. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2021 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2021 Year: 2022 Document Type: Article