An evolutionary artificial neural networks approach for breast cancer diagnosis

被引：263
|
作者：
Abbass, HA [1 ]
机构：
[1] Univ New S Wales, Sch Comp Sci, Canberra, ACT 2600, Australia
来源：
ARTIFICIAL INTELLIGENCE IN MEDICINE | 2002年 / 25卷 / 03期
关键词：
pareto optimization; differential evolution; artificial neural networks; breast cancer;
D O I：
10.1016/S0933-3657(02)00028-3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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). Axtificial 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 resuits against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：265 / 281
页数：17
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