Open Access Open Access  Restricted Access Subscription or Fee Access

Extracting Rules from Feed Forward Neural Networks for Diagnosing Breast Cancer

F. Paulin, Dr. A. Santhakumaran

Abstract


Medical diagnosis is one of the major problems in medical application. This includes the limitation of human expertise to diagnose disease manually. Extensive amount of knowledge and data stored in medical databases require specialized tools for analysis and effective usage of data. Breast cancer is the largest cause of cancer deaths among women. The proposed method uses Feed Forward Neural Network (FFNN) model to diagnose breast cancer. The  performance of the neural network is increased by 98%. The diagnosis of breast cancer is examined on the well known and widely accepted Wisconsin breast cancer data (WBCD). The proposed method classifies whether a tumor is benign or malignant by five rules using Back propagation algorithm.


Keywords


Artificial Neural Network, Back Propagation Algorithm, Breast Cancer, Wisconsin Breast Cancer Data.

Full Text:

PDF

References


Tuba Kiyan And Tulay Yildirim, “Breast Cancer Diagnosis Using Statistical Neural Networks” Istanbul University, Journal Of Electrical And Electronics Engineering, Year 2004, vol. 4, Number 2, pp.1149-1153

Sudhir D. Swarkar, Ashok Ghatol, Amol P. Pande, “Neural Network Aided Breast Cancer Detection and Diagnosis Using Support Vector Machine” Proceedings of the International conference on Neural Networks, Cavtat, Croatia, June 12-14, 2006, pp. 158-163.

R. Setiono, “Extracting M-of-N rules from trained neural networks form trained neural networks”, IEEE transactions of Neural Networks, vol. 11, pp. 512-519, 2000

R. Setiono, “Extracting rules from neural networks by pruning and hidden unit node splitting,” Neural Computation vol. 9, 1997, pp. 205-225

L.Mangasarian and W.H.Wolberg: “Cancer diagnosis via linear programming”, SIAM News, vol. 23, Number 5, September 1990, pp. 1 & 18

Jun Zhang MS, Haobo Ma Md MS, “An Implementation of Guildford Cytological Grading System to diagnose Breast Cancer Using Naïve Bayesian Classifier”, MEDINFO 2004, M.Fieschi et al. (Eds),Amsterdam:IOS Press

A. Punitha, C.P.Sumathi and T. Santhanam, “A Combination of Genetic Algorithm and ART Neural Network for Breast Cancer Diagnosis” Asian Journal of Information Technology 6 (1):112-117, 2007, Medwell Journals, 2007.

S.M. Kamruzzaman and Md. Monirul Islam, “Extraction of Symbolic Rules from Artificial Neural Networks” Proceedings of world Academy of science, Engineering and Technology, vol. 10, Dec. 2005, ISSN 1307-6884

S. Rajasekaran, G.A. Vijayalakshmi Pai, Neural Networks, Fuzzy Logic, and Genetic Algorithms, Synthesis and Application, Prentice-Hall India, pp 11, 34.

S.N. Sivanandam, S. Sumathi, S.N. Deepa, Introduction to Neural Networks using Matlab 6.0 , Tata McGraw Hill, pp.185.

X. Yao and Y. Liu, “Neural Networks for Breast Cancer Diagnosis,” Proceedings of the 1999 Congress on Evolutionary Computation. vol. 3, 1999, pp. 1760-1767.

Y. Liu and X. Yao, “A cooperative Ensemble Learning System,” IEEE International Joint Conference on Neural Networks, vol.3,1998, pp. 2202-2207.

David B. Fogel, Eugene C.Wasson, Edward M.Boughton and Vincent W. Porto, “ A step toward computer-assisted mammography using evolutionary programming and neural networks”, Cancer Letters, vol. 119, Issue 1, pp. 93-97, 1997.

Charles E. Kahn, Jr, Linda M. Roberts, Katherine A. Shaffer and Peter Haddawy, “Construction of a Bayesian network for mammographic diagnosis of breast cancer”, Computers in Biology and Medicine, vol. 27, Issue 1, pp.19-29, 1997.

Shinsuke Morio, Satoru Kawahara, Naoyuki Okamoto, Tadao Suzuki, Takashi Okamoto, Masatoshi Haradas and Akio Shimizu, An expert system for early detection of cancer of the breast, Computers in Biology and Medicine, vol.19, Issue 5, pp. 295-305, 1989.


Refbacks

  • There are currently no refbacks.