Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach - 07/01/22
, Arzu Baygul b , Neslihan Gokmen c , Said Incir d , Cagdas Aktan e
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Abstract |
This 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.
Le texte complet de cet article est disponible en PDF.Keywords : COVID-19 symptoms, Severity of COVID-19, Clinical prognosis, Artificial intelligence, Machine learning, Feature selection, ICOMP
Plan
Vol 70 - N° 1
Article 103319- janvier 2022 Retour au numéro
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