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

doi:10.1109/IESTEC46403.2019.00106

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	562-567
Number of pages	6
ISBN (Electronic)	9781728116914
DOIs	https://doi.org/10.1109/IESTEC46403.2019.00106
State	Published - Oct 2019
Externally published	Yes
Event	7th International Engineering, Sciences and Technology Conference, IESTEC 2019 - Panama City, Panama Duration: 9 Oct 2019 → 11 Oct 2019

Publication series

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

Conference

Conference	7th International Engineering, Sciences and Technology Conference, IESTEC 2019
Country/Territory	Panama
City	Panama City
Period	9/10/19 → 11/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Artificial intelligence
Backpropagation
Cardiac diseases
Early detection
Neural networks

Access to Document

10.1109/IESTEC46403.2019.00106

Cite this

Calderon-Vilca, H. D., Callupe, K. E. C., Aliaga, R. J. I., Cuba, J. B., & Marino-Cardenas, F. C. (2019). Early cardiac disease detection using neural networks. In Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019 (pp. 562-567). Article 8943663 (Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IESTEC46403.2019.00106

Calderon-Vilca, Hugo D. ; Callupe, Kevin E.Chagua ; Aliaga, Richard J.Inga et al. / Early cardiac disease detection using neural networks. Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 562-567 (Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019).

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title = "Early cardiac disease detection using neural networks",

abstract = "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.",

keywords = "Artificial intelligence, Backpropagation, Cardiac diseases, Early detection, Neural networks",

author = "Calderon-Vilca, {Hugo D.} and Callupe, {Kevin E.Chagua} and Aliaga, {Richard J.Inga} and Cuba, {Jair Barzola} and Marino-Cardenas, {Flor C.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 7th International Engineering, Sciences and Technology Conference, IESTEC 2019 ; Conference date: 09-10-2019 Through 11-10-2019",

year = "2019",

month = oct,

doi = "10.1109/IESTEC46403.2019.00106",

language = "Ingl{\'e}s",

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Calderon-Vilca, HD, Callupe, KEC, Aliaga, RJI, Cuba, JB & Marino-Cardenas, FC 2019, Early cardiac disease detection using neural networks. in Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019., 8943663, Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 562-567, 7th International Engineering, Sciences and Technology Conference, IESTEC 2019, Panama City, Panama, 9/10/19. https://doi.org/10.1109/IESTEC46403.2019.00106

Early cardiac disease detection using neural networks. / Calderon-Vilca, Hugo D.; Callupe, Kevin E.Chagua; Aliaga, Richard J.Inga et al.
Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 562-567 8943663 (Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Calderon-Vilca, Hugo D.

AU - Callupe, Kevin E.Chagua

AU - Aliaga, Richard J.Inga

AU - Cuba, Jair Barzola

AU - Marino-Cardenas, Flor C.

PY - 2019/10

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AB - 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.

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KW - Backpropagation

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Calderon-Vilca HD, Callupe KEC, Aliaga RJI, Cuba JB, Marino-Cardenas FC. Early cardiac disease detection using neural networks. In Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 562-567. 8943663. (Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019). doi: 10.1109/IESTEC46403.2019.00106

Early cardiac disease detection using neural networks

Abstract

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Bibliographical note

Keywords

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