Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFOP |
Texto Completo: | http://www.repositorio.ufop.br/jspui/handle/123456789/15797 https://doi.org/10.1016/j.compbiomed.2021.104335 |
Resumo: | The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1–score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by- case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system. |
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Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs.Explainable artificial intelligenceThe sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1–score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by- case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system.2022-11-10T21:00:20Z2022-11-10T21:00:20Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfALVES, M. A. et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Computers in Biology and Medicine, v. 132, artigo 104335, 2021. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0010482521001293>. Acesso em: 06 jul. 2022.0010-4825http://www.repositorio.ufop.br/jspui/handle/123456789/15797https://doi.org/10.1016/j.compbiomed.2021.104335This article is made available under the Elsevier license (http://www.elsevier.com/open-access/userlicense/1.0/). Fonte: o PDF do artigo.info:eu-repo/semantics/openAccessAlves, Marcos AntonioCastro, Giulia Zanon deOliveira, Bruno Alberto SoaresFerreira, Leonardo AugustoRamírez, Jaime ArturoSilva, Rodrigo César PedrosaGuimarães, Frederico Gadelhaengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOP2022-11-10T21:00:27Zoai:repositorio.ufop.br:123456789/15797Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332022-11-10T21:00:27Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false |
dc.title.none.fl_str_mv |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
title |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
spellingShingle |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Alves, Marcos Antonio Explainable artificial intelligence |
title_short |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
title_full |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
title_fullStr |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
title_full_unstemmed |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
title_sort |
Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. |
author |
Alves, Marcos Antonio |
author_facet |
Alves, Marcos Antonio Castro, Giulia Zanon de Oliveira, Bruno Alberto Soares Ferreira, Leonardo Augusto Ramírez, Jaime Arturo Silva, Rodrigo César Pedrosa Guimarães, Frederico Gadelha |
author_role |
author |
author2 |
Castro, Giulia Zanon de Oliveira, Bruno Alberto Soares Ferreira, Leonardo Augusto Ramírez, Jaime Arturo Silva, Rodrigo César Pedrosa Guimarães, Frederico Gadelha |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Alves, Marcos Antonio Castro, Giulia Zanon de Oliveira, Bruno Alberto Soares Ferreira, Leonardo Augusto Ramírez, Jaime Arturo Silva, Rodrigo César Pedrosa Guimarães, Frederico Gadelha |
dc.subject.por.fl_str_mv |
Explainable artificial intelligence |
topic |
Explainable artificial intelligence |
description |
The sudden outbreak of coronavirus disease 2019 (COVID-19) revealed the need for fast and reliable automatic tools to help health teams. This paper aims to present understandable solutions based on Machine Learning (ML) techniques to deal with COVID-19 screening in routine blood tests. We tested different ML classifiers in a public dataset from the Hospital Albert Einstein, São Paulo, Brazil. After cleaning and pre-processing the data has 608 patients, of which 84 are positive for COVID-19 confirmed by RT-PCR. To understand the model decisions, we introduce (i) a local Decision Tree Explainer (DTX) for local explanation and (ii) a Criteria Graph to aggregate these explanations and portrait a global picture of the results. Random Forest (RF) classifier achieved the best results (accuracy 0.88, F1–score 0.76, sensitivity 0.66, specificity 0.91, and AUROC 0.86). By using DTX and Criteria Graph for cases confirmed by the RF, it was possible to find some patterns among the individuals able to aid the clinicians to understand the interconnection among the blood parameters either globally or on a case-by- case basis. The results are in accordance with the literature and the proposed methodology may be embedded in an electronic health record system. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 2022-11-10T21:00:20Z 2022-11-10T21:00:20Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
ALVES, M. A. et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Computers in Biology and Medicine, v. 132, artigo 104335, 2021. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0010482521001293>. Acesso em: 06 jul. 2022. 0010-4825 http://www.repositorio.ufop.br/jspui/handle/123456789/15797 https://doi.org/10.1016/j.compbiomed.2021.104335 |
identifier_str_mv |
ALVES, M. A. et al. Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs. Computers in Biology and Medicine, v. 132, artigo 104335, 2021. Disponível em: <https://www.sciencedirect.com/science/article/pii/S0010482521001293>. Acesso em: 06 jul. 2022. 0010-4825 |
url |
http://www.repositorio.ufop.br/jspui/handle/123456789/15797 https://doi.org/10.1016/j.compbiomed.2021.104335 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFOP instname:Universidade Federal de Ouro Preto (UFOP) instacron:UFOP |
instname_str |
Universidade Federal de Ouro Preto (UFOP) |
instacron_str |
UFOP |
institution |
UFOP |
reponame_str |
Repositório Institucional da UFOP |
collection |
Repositório Institucional da UFOP |
repository.name.fl_str_mv |
Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP) |
repository.mail.fl_str_mv |
repositorio@ufop.edu.br |
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1813002858202660864 |