•  
  •  
 

International Journal of Computer and Communication Technology

Abstract

The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.