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

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.

SUBMITTER: Bergamini M

PROVIDER: S-EPMC7728276 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Bergamini Marcela M Iora Pedro Henrique PH Rocha Thiago Augusto Hernandes TAH Tchuisseu Yolande Pokam YP Dutra Amanda de Carvalho AC Scheidt João Felipe Herman Costa JFHC Nihei Oscar Kenji OK de Barros Carvalho Maria Dalva MD Staton Catherine Ann CA Vissoci João Ricardo Nickenig JRN de Andrade Luciano L

PloS one 20201210 12

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 Paran ...[more]

PMID: 33301451

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Project description:BackgroundAlthough the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis.MethodsAll statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to identify differentially expressed genes (DEGs) between CAD samples and healthy controls. The WGCNA package was conducted to recognize CAD-related gene modules and hub genes, followed by recursive feature elimination analysis to select the optimal features genes (OFGs). The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. Further validation and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the classification performance.ResultsIn total, 374 DEGs, eight gene modules, 33 hub genes and 12 OFGs (HTR4, KISS1, CA12, CAMK2B, KLK2, DDC, CNGB1, DERL1, BCL6, LILRA2, HCK, MTF2) were identified. ROC curve analysis showed that the accuracy of SVM, RF and LR were 75.58%, 63.57% and 63.95% in validation; with area under the curve of 0.813 (95% confidence interval, 95% CI 0.761-0.866, P < 0.0001), 0.727 (95% CI 0.665-0.788, P < 0.0001) and 0.783 (95% CI 0.725-0.841, P < 0.0001), respectively.ConclusionsIn conclusion, this study found 12 gene signatures involved in the pathogenic mechanism of CAD. Among the CAD classifiers constructed by three machine learning methods, the SVM model has the best performance.

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

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

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