Explainable Artificial Intelligence for COVID-19 Diagnosis Through Blood Test Variables

This work proposes an explainable artificial intelligence approach to help diagnose COVID-19 patients based on blood test and pathogen variables. Two glass-box models, logistic regression and explainable boosting machine, and two black-box models, random forest and support vector machine, were used...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2022-04, Vol.33 (2), p.625-644
Main Authors: Thimoteo, Lucas M., Vellasco, Marley M., Amaral, Jorge, Figueiredo, Karla, Yokoyama, Cátia Lie, Marques, Erito
Format: Article
Language:English
Subjects:
Artificial intelligence
Blood tests
Control
Control and Systems Theory
Coronaviruses
COVID-19
Diagnosis
Electrical Engineering
Engineering
Eosinophils
Explainable artificial intelligence
Leukocytes
Mathematical models
Mechatronics
Robotics
Robotics and Automation
Support vector machines
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Summary:This work proposes an explainable artificial intelligence approach to help diagnose COVID-19 patients based on blood test and pathogen variables. Two glass-box models, logistic regression and explainable boosting machine, and two black-box models, random forest and support vector machine, were used to assess the disease diagnosis. Shapley additive explanations were used to explain predictions for the black-box models, while glass-box models feature importance brought insights into the most relevant features. All global explanations show the eosinophils and leukocytes, white blood cells are among the essential features to help diagnose the COVID-19. Moreover, the best model obtained an AUC of 0.87.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-021-00858-y