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.
|Title of host publication||Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Oct 2019|
|Event||7th International Engineering, Sciences and Technology Conference, IESTEC 2019 - Panama City, Panama|
Duration: 9 Oct 2019 → 11 Oct 2019
|Name||Proceedings - 2019 7th International Engineering, Sciences and Technology Conference, IESTEC 2019|
|Conference||7th International Engineering, Sciences and Technology Conference, IESTEC 2019|
|Period||9/10/19 → 11/10/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- Artificial intelligence
- Cardiac diseases
- Early detection
- Neural networks