Publication: Clinical prognosis evaluation of COVID-19 patients: an interpretable hybrid machine learning approach
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Program
KU-Authors
KU Authors
Co-Authors
Kocadağlı, O.
Gökmen, N.
Aktan C.
Advisor
Publication Date
Language
English
Type
Journal Title
Journal ISSN
Volume Title
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.
Description
Source:
Current Research in Translational Medicine
Publisher:
Elsevier
Keywords:
Subject
Radiological findings, Clinical features, COVID-19