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Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests PurposeA new kind of coronavirus, the SARS-CoV-2, started the biggest pandemic of the century. More than a million people have been killed by Covid-19. Because of this, quick and precise diagnosis test is necessary. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results take too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact on hematological parameters.MethodThis work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. Laboratory parameters obtained from the hemogram and biochemical tests defined as standards to support clinical diagnosis were used as input features. Afterward, we used particle swarm optimization, evolutionary algorithms, and manual selection based on cost minimization to select the most significant features.ResultsWe tested several machine learning methods, and we achieved high classification performance: overall accuracy of 95.159% ± 0.693, kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010, and specificity of 0.936 ± 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed.ConclusionThis points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Heg.IA: an intelligent system to support diagnosis of Covid-19 based on blood tests

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Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2021
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00112-5
Publisher site
See Article on Publisher Site

Abstract

PurposeA new kind of coronavirus, the SARS-CoV-2, started the biggest pandemic of the century. More than a million people have been killed by Covid-19. Because of this, quick and precise diagnosis test is necessary. The current gold standard is the RT-PCR with DNA sequencing and identification, but its results take too long to be available. Tests base on IgM/IgG antibodies have been used, but their sensitivity and specificity may be very low. Many studies have been demonstrating the Covid-19 impact on hematological parameters.MethodThis work proposes an intelligent system to support Covid-19 diagnosis based on blood testing. Laboratory parameters obtained from the hemogram and biochemical tests defined as standards to support clinical diagnosis were used as input features. Afterward, we used particle swarm optimization, evolutionary algorithms, and manual selection based on cost minimization to select the most significant features.ResultsWe tested several machine learning methods, and we achieved high classification performance: overall accuracy of 95.159% ± 0.693, kappa index of 0.903 ± 0.014, sensitivity of 0.968 ± 0.007, precision of 0.938 ± 0.010, and specificity of 0.936 ± 0.011. These results were achieved using classical and low computational cost classifiers, with Bayes Network being the best of them. In addition, only 24 blood tests were needed.ConclusionThis points to the possibility of a new rapid test with low cost. The desktop version of the system is fully functional and available for free use.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Mar 1, 2022

Keywords: Covid-19; Blood tests; Software-based rapid test; Computer-aided diagnosis; Machine learning for diagnosis; Covid-19 rapid test

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