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Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm

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dc.contributor.author Deniz, Ayça
dc.contributor.author Kızılöz, Hakan Ezgi
dc.contributor.author Sevinç, Ender
dc.contributor.author Dökeroğlu, Tansel
dc.date.accessioned 2024-05-08T08:25:18Z
dc.date.available 2024-05-08T08:25:18Z
dc.date.issued 2022-06
dc.identifier.citation Deniz, Ayça;...et.al. (2022). "Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm", Expert Systems, Vol.39, No.5. tr_TR
dc.identifier.issn 02664720
dc.identifier.uri http://hdl.handle.net/20.500.12416/8182
dc.description.abstract The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1111/exsy.12949 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Classification tr_TR
dc.subject COVID-19 tr_TR
dc.subject Extreme Learning Machines tr_TR
dc.subject Feature Selection tr_TR
dc.subject Multi-Threaded Computation tr_TR
dc.title Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm tr_TR
dc.type article tr_TR
dc.relation.journal Expert Systems tr_TR
dc.contributor.authorID 234173 tr_TR
dc.identifier.volume 39 tr_TR
dc.identifier.issue 5 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü tr_TR


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