Use of Modular Neural Network for Heart Disease

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

Evolutionary radial basis function network for classificatory problems

Classification has been a major problem of study whose application includes speaker recognition, character recognition, etc. In this paper we first adapt the Radial Basis Function Network (RBFN) for classification problems and then use customized Evolutionary Algorithms to evolve the RBFN. The neurons of the RBFN correspond to some class out of the available output classes. Linear addition of only the same class neurons is taken and an additional layer is added that decides the final output on the basis of maximum activation of each class. Evolutionary algorithm has operators jump and add neuron that aid in optimization. Penalty has been used to restrict overgrowth of network. The algorithm was used to solve the problem of detection of PIMA Indian diabetes and gave a recognition rate of 82.37%, which was better than most of the commonly known algorithms in literature

Diagnosis of Breast Cancer by Modular Neural Network

Abstract-Diagnosis of diseases is well known problem in the medical field. Past research shows that medical database of disease can be train by using various neural network models. Many medical problems face the problem of curse of dimensionality due to the excessively large number of input attributes. Breast cancer is one such problem. We propose the use of modular neural network for effective diagnosis. In the proposed methodology four modules are made; each module gets half the problem attributes which are trained and tested by two neural network models, Back Propagation Neural Network (BPNN) and Radial Basis Function (RBFN). Integration is done using a probabilistic sum rule. The modular neural network gave an accuracy of 95.75% over training data and 98.22% over testing accuracy, which was experimentally determined to be better than monolithic neural networks