Diagnosis of breast cancer by modular neural network

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.