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A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence
Author(s) -
Belciug Smaranda,
Gorunescu Florin
Publication year - 2013
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2012.00635.x
Subject(s) - computer science , categorical variable , artificial neural network , feedforward neural network , genetic algorithm , multilayer perceptron , perceptron , set (abstract data type) , artificial intelligence , algorithm , hybrid algorithm (constraint satisfaction) , machine learning , data mining , pattern recognition (psychology) , constraint satisfaction , probabilistic logic , programming language , constraint logic programming
Genetic algorithms ( GA s) and neural networks ( NN s) are both inspired by computation in biological systems and many attempts have been made to combine the two methodologies to boost the NN s performance. This paper deals with the evolutionary training of a feedforward NN for both breast cancer detection and recurrence. A multi‐layer perceptron ( MLP ) has been designed for this purpose, using a GA routine to set weights, and a J ava 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 standard NN s, the performance of this approach using the same databases is shown to be superior. Moreover, this hybrid MLP / GA model is very flexible in terms of providing accurate classification, even with different types of attributes, which is usually found in medical studies.