A Genetic-Based Neuro-Fuzzy Generator: NEFGEN
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Analysis of Breast Cancer Using Data Mining and Statistical Techniques
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Evolving hierarchical RBF neural networks for breast cancer detection
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
MT-CGP: mixed type cartesian genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Breast cancer detection using cartesian genetic programming evolved artificial neural networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper we develop an integrated expert system for diagnosis, prognosis and prediction for breast cancer using soft computing techniques. The basic aim is to compare the various neural network models from the recent literature. Breast cancer database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository. Three different data sets have been used, each employing different diagnostic technique. It can use diagnosis, prognosis and survivability prediction of breast cancer patient in one intelligent system. We implement six models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization, Probabilistic Neural Networks, Recurrent Neural Network, and Competitive Neural network. Experimental Results show that different models give optimal performance for different types of data sets. However, all the models are able to solve the problem to a reasonable extent.