title: An evolutionary artificial neural networks approach for breast cancer diagnosis.
abstract: This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.
authors.affiliation: School of Computer Science, University of New South Wales, Australian Defence Force Academy Campus, Northcott Drive, 2600 Canberra, ACT, Australia. h.abbass@adfa.edu.au
authors.forename: Hussein A
authors.initials: HA
authors.lastname: Abbass
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003937:Diagnosis, Differential; D018450:Disease Progression; D005260:Female; D005544:Forecasting; D006801:Humans; D016571:Neural Networks, Computer