Diagnosis of breast cancer by modular evolutionary neural networks

We construct a mixture of experts model for medical diagnosis. Each of the experts is a complex modular neural network. The first modularity clusters the entire input space into a set of modules. The second modularity divides the number of attributes. Each cluster is a neural network that solves the problem. The individual neural networks are evolved using genetic algorithms, which optimise the architecture along with the weights and biases. The complete system is used for the diagnosis of breast cancer. Experimental results show that the proposed system outperforms the traditional simple and hybrid approaches.