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Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests.

Authors
  • Çubukçu, Hikmet Can1
  • Topcu, Deniz İlhan2
  • Bayraktar, Nilüfer2
  • Gülşen, Murat3
  • Sarı, Nuran4
  • Arslan, Ayşe Hande4
  • 1 Interdisciplinary Stem Cells and Regenerative Medicine, Ankara University Stem Cell Institute, Ankara, Turkey. , (Turkey)
  • 2 Departments of Medical Biochemistry and Clinical Microbiology, Başkent University Faculty of Medicine, Ankara, Turkey. , (Turkey)
  • 3 Department of Autism, Special Mental Needs and Rare Diseases Department, Turkish Ministry of Health, Ankara, Turkey. , (Turkey)
  • 4 Department of Infectious Diseases and Clinical Microbiology, Başkent University Faculty of Medicine, Ankara, Turkey. , (Turkey)
Type
Published Article
Journal
American Journal of Clinical Pathology
Publisher
Oxford University Press
Publication Date
May 04, 2022
Volume
157
Issue
5
Pages
758–766
Identifiers
DOI: 10.1093/ajcp/aqab187
PMID: 34791032
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results. We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil. The accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%). ML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses. © American Society for Clinical Pathology, 2021. All rights reserved. For permissions, please e-mail: [email protected]

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