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The Modified Perceptron Algorithm for Breast Cancer Diagnosis

Amal M. El_Nawasany, Ahmed Fouad, M. EL-Sayed Waheed


Breast cancer is the most common cancer in women worldwide. It is also the principle cause of death from cancer among women globally. Early detection of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Classification of breast cancer as malignant or benign can be achieved using many techniques. Artificial neural network (ANN) is one of these techniques. This paper presents a new ANN algorithm named as modified perceptron algorithm for data classification (MPADC) has been formed to classify a nonlinearly separable data. A new termination criterion is added to the original perceptron algorithm by using evaluation vector in order to monitor the accuracy that helps in setting predefine termination accuracy interval to avoid endless execution times as if the classification problems are not linearly separable then percerptron will be executed infinite number of times. In order to detect the classification accuracy of the proposed algorithm, we apply it to the Wisconsin breast cancer original database (WBCD) data. The average classification accuracy of the developed MPADC algorithm is 99.54%. Among the studies use neural network classification algorithms for the same breast cancer database, our algorithm appears to be very promising.The results suggest that MPADC is faster, more feasible and more accurate classification algorithm than the results obtained from related previous algorithms.


Artificial Neural Network(ANN), Perceptron Algorithm, Wisconsin Breast Cancer (Original) Database (WBCD).

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L. L. Ivanov, J. Hu, and A. Leak (2010). “Immigrant women‟s cancer screening behaviors”, Journal of Community Health Nursing, vol. 27, no.1, 32-35. DOI: 10.1080/07370010903466163.

S. R. Lodha et al, “Risk Factors for Breast Cancer among Women in Bhopal Urban Agglomerate: A Case-Control Study”, Asian Pacific J Cancer Prev, 12, PP. 2111-2115.

B. Lairenjam and S. K. Wasan, “Neural Network with Classification Based on Multiple Association Rule for Classifying Mammographic Data”, Proceeding IDEAL 2009 Proceedings of the 10th international conference on Intelligent data engineering and automated learning Pages 465-476 , Springer-Verlag Berlin, ISBN:3-642-04393-3 978-3-642-04393-2,2009.

F. Siraj, A. Ehab, A. Omer and R. Hasan (2012). “Data Mining and Neural Networks: The Impact of Data Representation, Advances in Data Mining Knowledge Discovery and Applications”, Associate Prof. Adem Karahoca (Ed.), ISBN: 978-953-51-0748-4, InTech, DOI: 10.5772/51594. Available from:

N. Ganesan, K. Venkatesh, M. A. Rama and A. M. Palani, “APPLICATION OF NEURAL NETWORKS in DIAGNOSING CANCER DISEASE using Demographic Data”, International Journal of Computer Applications (0975 - 8887) Volume 1 – No. 26, 2010.

S. Swathi, S. Rizwana, G. A. Babu, P. S. Kumar and P.V.G.K. Sarma, “Classification Of Neural Network Structures For Brea St Cancer Diagnosis”, International Journal of Computer Science and Communication, Vol. 3, No. 1, January-June 2012, pp. 227-231.

G. I. Salama, M.B. Abdelhalim, and M. A. Zeid, “Breast Cancer Diagnosis on Three Different Datasets Using Multi-Classifiers”, International Journal of Computer and Information Technology (2277 – 0764) Vol. 01– Issue 01, September 2012.

S. Saxena and K. Burse, “A Survey on Neural Network Techniques for Classification of Breast Cancer Data”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, vol.2, Issue-1, October 2012.

K. Mehrotra, C. K. Mohan and S. Ranka, “Elements of Artificial Neural Networks”, October 1996.

R. Rojas: “Neural Networks: A Systematic Introduction”, Springer-Verlag, Berlin, 1996., accessed at 26 September 2013.

T. Kıyan and T. Yıldırım, “BREAST CANCER DIAGNOSIS USING STATISTICAL NEURAL NETWORKS”, International XII. Turkish Symposium on Artificial Intelligence and Neural Networks – TAINN 2003.

S. Haykin, Neural Networks: A Comprehensive Foundation, Mac Millan College Publishing Company, 1994.

T. Anthony, C. Goh, Probabilistic neural network for seismic liquefaction potential, NRC Research Press Web site, 2002.

V. Cheung, K. Cannons,” An Introduction to Probabilistic Neural Networks”, University of Manitoba, Winnipeg, Manitoba, Canada, June 10, 2002.

R. G. Ahangar, M. Yahyazadehfar, H. Pournaghshband, “The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 2, February 2010.

I. Anagnostopoulos, C. Anagnostopoulos, A. Rouskas, G. Kormentzas and D. Vergados “The Wisconsin breast cancer problem: diagnosis and DFS time prognosis using probabilistic and generalised regression neural classifiers” Draft Version of paper to appeared in the Oncology Reports, special issue Computational Analysis and Decision Support Systems in Oncology, last quarter 2005.

C. Lu, J. De Brabanter, S. Van Huffel, I. Vergote, D. Timmerman, “Using artificial neural networks to predict malignancy of ovarian tumors”, 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001.

S. A. Mojarad, S. S. Dlay, W. L. Woo, G. V. Sherbet, ”Breast Cancer prediction and cross validation using multilayer perceptron neural networks”, Proceedings 7th Communication Systems Networks and Digital Signal Processing (CSNDSP-2010), 21st-23rd July, IEEE, Newcastle Upon Tyne. pp: 760-764, 2010.

S. S. Haykin, Neural Networks and Learning Machines, 3rd ed. New York: Prentice Hall, 2009, pp. 906.

S. Mojarad, S. S. Dlay, W. L. Woo, and G. V. Sherbet “Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks” American J. of Eng. and Applied Sci., vol. 5, no 1 pp 42-51, 2012.

F. Paulin and A. Santhakumaran, “Classification of breast cancer by comparing back propagation training algorithms”, Int. J. on Comput. Sci. and Eng. (IJCSE), vol. 3, no. 1, Jan. 2011.

R. Nithya and B. Santhi, “Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer” Int. J. of Comput. Applicat, vol. 28, no.6, pp. 0975 – 8887, Aug. 2011.

E. Antipov and E. Pokryshevskaya, “Applying CHAID for logistic regression diagnostics and classication accuracy improvement”, The State University Higher School of Economics, MPRA Munich Personal RePEc Archive, 2009.

Y. Chen, A. Abraham and B. Yang, “Feature Selection and Classification using Flexible Neural Tree”, Journal of Neurocomputing vol. 70, no. (1-3), pp. 305–313, 2006., accessed at 26 September 2013.

D. Dumitru, “Prediction of recurrent events in breast cancer using the Naive Bayesian classification”, Annals of University of Craiova, Math. Comp. Sci. Ser. Vol. 36, no. 2, PP. 92-96, ISSN: 1223-6934. 2009.

Y. A. Christobel and Dr. Sivaprakasam, “An Empirical Comparison of Data Mining Classification Methods”, International Journal of Computer Information Systems, Vol. 3, No. 2, PP. 24-28, 2011.

J. Han and M. Kamber,”Data Mining Concepts and Techniques”, Morgan Kauffman Publishers, USA, 2006.

R. Quinlan, “C4.5: Programs for Machine Learning”, Morgan Kaufmann Publishers, San Mateo, CA, 1993.

V. N. Vapnik, “The Nature of Statistical Learning Theory”, 1st ed., Springer-Verlag, New York, 1995., accessed at 26 September 2013.

P. P. Gallego, J. Gago and M.(2011). Artificial Neural Networks Technology to Model and Predict Plant Biology Process, Artificial Neural Networks - Methodological Advances and Biomedical Applications, Prof. Kenji Suzuki (Ed.), ISBN: 978-953-307-243-2, InTech, Available from:

P. Tahmasebi, A. Hezarkhani, “Application of Adaptive Neuro-Fuzzy Inference System for Grade Estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran”, Australian Journal of Basic and Applied Sciences, 4(3): 408-420, ISSN 1991-8178, 2010.

C.Loganathan, K.V.Girija, “Hybrid Learning For Adaptive Neuro Fuzzy Inference System”, Research Inventy: International Journal Of Engineering And Science, Vol.2, Issue 11, Pp 06-13.

Hazlina Hamdan and Jonathan M. Garibaldi, Member, “Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival”, WCCI 2010 IEEE World Congress on Computational Intelligence, July, 18-23, CCIB, Barcelona, Spain, 2010.

M. Negnevitsky, “Artificial Intelligence: a guide to intelligent systems”, Essex, England: Pearson Education Limited, 2005.

J. S. Jang, “Anfis adaptive-network-based fuzzy inference system,” Systems, Man and Cybernetics, IEEE Transactions on, vol. 23, pp. 665–685, May/Jun 1993.

G. Arulampalam and A. Bouzerdoum, “Application of shunting inhibitory artificial neural networks to medical diagnosis”, Proceedings of 7th Australian and New Zealand Intelligent Information Systems Conference. (pp. 89 - 94). Australia, IEEE, 2001

C. M. Taylor, “Selecting Neural Network Topologies: A Hybrid Approach Combining Genetic Algorithms and Neural Networks”, Master of Science, University of Kansas, 1997.

R. E. Weeks and J. M. Burgess, “Evolving artificial neural networks to control chaotic systems”, The American Physical Society, Physical Review E, vol. 56, no. 2, pp 1531-1540, August 1997.

T. Praczyk, “Adaptation of symbiotic adaptive neuroevolution in assembler encoding”, Theoretical and Applied Informatics, ISSN 1896–5334, Vol. 20, no. 1, pp. 49–6, 2008.

Q. Shao, R. C. Rowe and P. York, “Comparison of neurofuzzy logic and neural networks in modelling experimental data of an immediate release tablet formulation”, European Journal of Pharmaceutical Science, vol. 28, pp 394-404, 2006, accessed at 26 Sep 2013.

William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196.


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