Early cardiac disease detection using neural networks

Hugo D. Calderon-Vilca, Kevin E.Chagua Callupe, Richard J.Inga Aliaga, Jair Barzola Cuba, Flor C. Marino-Cardenas

Resultado de la investigación: Contribución a una conferenciaArtículo

3 Citas (Scopus)

Resumen

© 2019 IEEE. Heart disease is one of the biggest problems in the world that will continue to be research. We have made an overview of the research related to heart disease. We verify that algorithms have been used for the classification of cardiac diseases: Apriori, decision tree, naive Bayesian. Neural network, SVM, ANN, KN and others. In this research, we have designed three architectures of neural networks to evaluate which of them adapts and predicts better the presence of heart diseases, we have determined that the architecture that best adapts is a neural network Backpropagation with quadratic error 0.01788 with a 99.26% accuracy. In addition, we have designed a web application tool to detect heart disease, this tool has been designed with the steps of software engineering.
Idioma originalInglés estadounidense
Páginas562-567
Número de páginas6
DOI
EstadoPublicada - 1 oct 2019
EventoProceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019 -
Duración: 1 oct 2019 → …

Conferencia

ConferenciaProceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019
Período1/10/19 → …

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