Mapping risk of ischemic heart disease using machine learning in a Brazilian state

Marcela Bergamini, Pedro Henrique Iora, Thiago Augusto Hernandes Rocha, Yolande Pokam Tchuisseu, Amanda de Carvalho Dutra, João Felipe Herman Costa Scheidt, Oscar Kenji Nihei, Maria Dalva de Barros Carvalho, Catherine Ann Staton, João Ricardo Nickenig Vissoci and Luciano de Andrade

PLOS ONE, 2020, vol. 15, issue 12, 1-15

Abstract: 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.

Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243558 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 43558&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0243558

DOI: 10.1371/journal.pone.0243558

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().