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International Journal of Innovation and Applied Studies
ISSN: 2028-9324     CODEN: IJIABO     OCLC Number: 828807274     ZDB-ID: 2703985-7
 
 
Sunday 22 July 2018

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A New Generalized Regression Artificial Neural Networks Approach for Diagnosing Heart Disease


Volume 4, Issue 4, December 2013, Pages 679–689

 A New Generalized Regression Artificial Neural Networks Approach for Diagnosing Heart Disease

Behnam Zebardast1, Ali Ghaffari2, and Mohammad Masdari3

1 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, West Azerbaijan, Iran
2 Department of Computer Engineering, Tabriz Branch, Islamic Azad University Tabriz, Iran
3 Department of Computer Engineering, Science and Research Branch, Islamic Azad University, West Azerbaijan, Iran

Original language: English

Received 20 October 2013

Copyright © 2013 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Artificial Neural Networks (ANNs) play an important role in the field of medical science in solving health problems and diagnosing diseases both in critical illnesses and in common diseases. Since it is important to diagnose accurately the people' disease condition, therefore for the precisely diagnosing those condition, we must use appropriate methods that to minimize the errors in diagnosis. So, using an appropriate method to diagnose heart disease and to prevent complications of the disease is an important step toward patients' improvement. Therefore, in this paper the presence or the absence of heart disease of the four datasets using Generalized Regression Neural Networks (GRNN) will be discussed. Each of the four datasets contains of 14 features that they are used to diagnose heart disease with GRNN. In this paper, GRNN have been implemented in MATLAB environment. The aim is maximizing the precision of measurement in accurately diagnosing heart disease in the process of training and testing. By comparing the results of each dataset, we found the best accuracy in the training phase that is equal to 100% which belongs to Switzerland and Long Beach VA datasets, and the best accuracy in the testing phase belongs to the Cleveland dataset that is equal to 96.6667%.

Author Keywords: Generalized Regression Neural Network (GRNN), heart disease, datasets, training accuracy, test accuracy.


How to Cite this Article


Behnam Zebardast, Ali Ghaffari, and Mohammad Masdari, “A New Generalized Regression Artificial Neural Networks Approach for Diagnosing Heart Disease,” International Journal of Innovation and Applied Studies, vol. 4, no. 4, pp. 679–689, December 2013.