Early cardiac disease detection using neural networks

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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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
Título de la publicación alojadaProceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas562-567
Número de páginas6
ISBN (versión digital)9781728116914
DOI
EstadoPublicada - oct 2019
Publicado de forma externa
Evento7th International Engineering, Sciences and Technology Conference, IESTEC 2019 - Panama City, Panamá
Duración: 9 oct 201911 oct 2019

Serie de la publicación

NombreProceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019

Conferencia

Conferencia7th International Engineering, Sciences and Technology Conference, IESTEC 2019
PaísPanamá
CiudadPanama City
Período9/10/1911/10/19

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