A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence
Smaranda Belciug, Florin Gorunescu · Expert Systems · 2012
Abstract Genetic algorithms (GAs) and neural networks (NNs) are both inspired by computation in biological systems and many attempts have been made to combine the two methodologies to boost theNNs performance. This paper deals with the evolutionary training of a feedforwardNNfor both breast cancer detection and recurrence. A multi‐layer perceptron (MLP) has been designed for this purpose, using aGAroutine to set weights, and aJava implementation of this hybrid model has been made. Four databases concerning cancer detection and recurrence have been used, two databases containing numerical attributes only, one database containing ordinal (categorical) attributes solely and one database with mixed attributes. In comparison to some standardNNs, the performance of this approach using the same databases is shown to be superior. Moreover, this hybridMLP/GAmodel is very flexible in terms of providing accurate classification, even with different types of attributes, which is usually found in medical studies.