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Mapping risk of ischemic heart disease using machine learning in a Brazilian state
Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD ba...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728276/ https://www.ncbi.nlm.nih.gov/pubmed/33301451 http://dx.doi.org/10.1371/journal.pone.0243558 |
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author | Bergamini, Marcela Iora, Pedro Henrique Rocha, Thiago Augusto Hernandes Tchuisseu, Yolande Pokam Dutra, Amanda de Carvalho Scheidt, João Felipe Herman Costa Nihei, Oscar Kenji de Barros Carvalho, Maria Dalva Staton, Catherine Ann Vissoci, João Ricardo Nickenig de Andrade, Luciano |
author_facet | Bergamini, Marcela Iora, Pedro Henrique Rocha, Thiago Augusto Hernandes Tchuisseu, Yolande Pokam Dutra, Amanda de Carvalho Scheidt, João Felipe Herman Costa Nihei, Oscar Kenji de Barros Carvalho, Maria Dalva Staton, Catherine Ann Vissoci, João Ricardo Nickenig de Andrade, Luciano |
author_sort | Bergamini, Marcela |
collection | PubMed |
description | Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran’s I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities’ guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná. |
format | Online Article Text |
id | pubmed-7728276 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77282762020-12-17 Mapping risk of ischemic heart disease using machine learning in a Brazilian state Bergamini, Marcela Iora, Pedro Henrique Rocha, Thiago Augusto Hernandes Tchuisseu, Yolande Pokam Dutra, Amanda de Carvalho Scheidt, João Felipe Herman Costa Nihei, Oscar Kenji de Barros Carvalho, Maria Dalva Staton, Catherine Ann Vissoci, João Ricardo Nickenig de Andrade, Luciano PLoS One Research Article Cardiovascular diseases are the leading cause of deaths globally. Machine learning studies predicting mortality rates for ischemic heart disease (IHD) at the municipal level are very limited. The goal of this paper was to create and validate a Heart Health Care Index (HHCI) to predict risk of IHD based on location and risk factors. Secondary data, geographical information system (GIS) and machine learning were used to validate the HHCI and stratify the IHD municipality risk in the state of Paraná. A positive spatial autocorrelation was found (Moran’s I = 0.6472, p-value = 0.001), showing clusters of high IHD mortality. The Support Vector Machine, which had an RMSE of 0.789 and error proportion close to one (0.867), was the best for prediction among eight machine learning algorithms after validation. In the north and northwest regions of the state, HHCI was low and mortality clusters patterns were high. By creating an HHCI through ML, we can predict IHD mortality rate at municipal level, identifying predictive characteristics that impact health conditions of these localities’ guided health management decisions for improvements for IHD within the emergency care network in the state of Paraná. Public Library of Science 2020-12-10 /pmc/articles/PMC7728276/ /pubmed/33301451 http://dx.doi.org/10.1371/journal.pone.0243558 Text en © 2020 Bergamini et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bergamini, Marcela Iora, Pedro Henrique Rocha, Thiago Augusto Hernandes Tchuisseu, Yolande Pokam Dutra, Amanda de Carvalho Scheidt, João Felipe Herman Costa Nihei, Oscar Kenji de Barros Carvalho, Maria Dalva Staton, Catherine Ann Vissoci, João Ricardo Nickenig de Andrade, Luciano Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
title | Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
title_full | Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
title_fullStr | Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
title_full_unstemmed | Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
title_short | Mapping risk of ischemic heart disease using machine learning in a Brazilian state |
title_sort | mapping risk of ischemic heart disease using machine learning in a brazilian state |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728276/ https://www.ncbi.nlm.nih.gov/pubmed/33301451 http://dx.doi.org/10.1371/journal.pone.0243558 |
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