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


KOCADAĞLI O., Baygul A., Gökmen İnan N., Incir S., AKTAN Ç.

CURRENT RESEARCH IN TRANSLATIONAL MEDICINE, vol.70, no.1, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 70 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1016/j.retram.2021.103319
  • Journal Name: CURRENT RESEARCH IN TRANSLATIONAL MEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: COVID-19 symptoms, Severity of COVID-19, Clinical prognosis, Artificial intelligence, Machine learning, Feature selection, ICOMP, CLASSIFICATION
  • Istanbul Technical University Affiliated: Yes

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.