Clinical Prognosis Evaluation of Covid-19 Patients: an Interpretable Hybrid Machine Learning Approach

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dc.authorid260725en_US
dc.contributor.authorAktan, Çağdaş
dc.contributor.author.;, ve diğer
dc.date.accessioned2021-12-23T12:22:34Z
dc.date.available2021-12-23T12:22:34Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThis retrospective cohort study deals with evaluating severity of COVID-19 cases on the first symptoms and blood-test results of infected patients admitted to Emergency Department of Koc University Hospital (Istanbul, Turkey). To figure out remarkable hematological characteristics and risk factors in the prognosis evaluation of COVID-19 cases, the hybrid machine learning (ML) approaches integrated with feature selection procedure based Genetic Algorithms and information complexity were used in addition to the multivariate statistical analysis. Specifically, COVID-19 dataset includes demographic features, symptoms, blood test results and disease histories of total 166 inpatients with different age and gender groups. Analysis results point out that the hybrid ML methods has brought out potential risk factors on the severity of COVID-19 cases and their impacts on the prognosis evaluation, accurately.en_US
dc.identifier.citationCurrent Research in Translational Medicine 70 (2022) 103319en_US
dc.identifier.doi10.1016/j.retram.2021.103319
dc.identifier.issn2073-8994
dc.identifier.pmid34768217en_US
dc.identifier.scopus2-s2.0-85118772867en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1016/j.retram.2021.103319
dc.identifier.wosWOS:000854000600003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectArtificial intelligenceen_US
dc.subjectCOVID-19en_US
dc.subjectClinical prognosisen_US
dc.subjectFeature selectionen_US
dc.subjectICOMPen_US
dc.subjectMachine learningen_US
dc.subjectSeverity of COVID-19en_US
dc.titleClinical Prognosis Evaluation of Covid-19 Patients: an Interpretable Hybrid Machine Learning Approachen_US
dc.typeArticleen_US

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