On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/86715 |
Resumo: | This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features—mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator—were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases. |
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On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disordersBiological indicatorsCardiopathyClassification modelsData scienceMachine learningResource efficiencyCiências Naturais::MatemáticasSaúde de qualidadeThis research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features—mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator—were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases.This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through grant number 303192/2022-4 (R.O.); by FONDECYT grant number 1200525 (V.L.) from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT-Research Centre of Mathematics of University of Minho within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.).MDPIUniversidade do MinhoOspina, RaydonalFerreira, Adenice G. O.Oliveira, Hélio M. deLeiva, VíctorCastro, Cecília2023-09-232023-09-23T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/86715eng2227-905910.3390/biomedicines111026042604https://www.mdpi.com/2227-9059/11/10/2604info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-14T01:20:43Zoai:repositorium.sdum.uminho.pt:1822/86715Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:35:26.340189Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
title |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
spellingShingle |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders Ospina, Raydonal Biological indicators Cardiopathy Classification models Data science Machine learning Resource efficiency Ciências Naturais::Matemáticas Saúde de qualidade |
title_short |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
title_full |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
title_fullStr |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
title_full_unstemmed |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
title_sort |
On the use of machine learning techniques and non-invasive indicators for classifying and predicting cardiac disorders |
author |
Ospina, Raydonal |
author_facet |
Ospina, Raydonal Ferreira, Adenice G. O. Oliveira, Hélio M. de Leiva, Víctor Castro, Cecília |
author_role |
author |
author2 |
Ferreira, Adenice G. O. Oliveira, Hélio M. de Leiva, Víctor Castro, Cecília |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Ospina, Raydonal Ferreira, Adenice G. O. Oliveira, Hélio M. de Leiva, Víctor Castro, Cecília |
dc.subject.por.fl_str_mv |
Biological indicators Cardiopathy Classification models Data science Machine learning Resource efficiency Ciências Naturais::Matemáticas Saúde de qualidade |
topic |
Biological indicators Cardiopathy Classification models Data science Machine learning Resource efficiency Ciências Naturais::Matemáticas Saúde de qualidade |
description |
This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository. Our findings highlight the ability of machine learning algorithms to not only streamline diagnostic procedures but also reduce diagnostic errors and the dependency on extensive clinical testing. Three key features—mean arterial pressure, pulsatile blood pressure index, and resistance-compliance indicator—were found to significantly improve the accuracy of machine learning algorithms in binary heart disease classification. Logistic regression achieved the highest average accuracy among the examined classifiers when utilizing these features. While such novel indicators contribute substantially to the classification process, they should be integrated into a broader diagnostic framework that includes comprehensive patient evaluations and medical expertise. Therefore, the present study offers valuable insights for leveraging data science techniques in the diagnosis and management of cardiovascular diseases. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-09-23 2023-09-23T00:00:00Z |
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 |
https://hdl.handle.net/1822/86715 |
url |
https://hdl.handle.net/1822/86715 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-9059 10.3390/biomedicines11102604 2604 https://www.mdpi.com/2227-9059/11/10/2604 |
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.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133617292574720 |