Publication:
Clinical prognosis evaluation of COVID-19 patients: an interpretable hybrid machine learning approach

Thumbnail Image

Organizational Units

Program

KU Authors

Co-Authors

Kocadağlı, O.
Gökmen, N.
Aktan C.

Advisor

Publication Date

Language

English

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

Citation

Endorsement

Review

Supplemented By

Referenced By

Copy Rights Note

0

Views

1

Downloads

View PlumX Details