Clinical prognosis evaluation of COVID-19 patients: An interpretable hybrid machine learning approach.

Autor: Kocadagli O; Department of Statistics, Mimar Sinan University, Silahsör Cad. No. 71, 34380 Bomonti/Sisli, Istanbul, Turkey. Electronic address: ozan.kocadagli@msgsu.edu.tr., Baygul A; Department of Biostatistics, Faculty of Medicine, Koc University, Turkey. Electronic address: abaygul@ku.edu.tr., Gokmen N; Neslihan Gokmen, Department of Basic Sciences, Istanbul Technical University, Turkey. Electronic address: ngokmen@itu.edu.tr., Incir S; Said Incir, Department of Biochemistry, Koc University Hospital, Turkey. Electronic address: sincir@ku.edu.tr., Aktan C; Department of Medical Biology, Beykent University, Turkey. Electronic address: cagdasaktan@beykent.edu.tr.
Jazyk: angličtina
Zdroj: Current research in translational medicine [Curr Res Transl Med] 2022 Jan; Vol. 70 (1), pp. 103319. Date of Electronic Publication: 2021 Oct 30.
DOI: 10.1016/j.retram.2021.103319
Abstrakt: 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.
Competing Interests: Conflict of Interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
(Copyright © 2021 Elsevier Masson SAS. All rights reserved.)
Databáze: MEDLINE
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