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Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence

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Abstract

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in clinical breast cancer diagnosis.

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References

  1. Subashini, T., Ramalingam, V., and Palanivel, S., Breast mass classification based on cytological patterns using RBFNN and SVM. Expert Syst Appl 36(3):5284–5290, 2009.

    Article  Google Scholar 

  2. Quinlan, J., Improved use of continuous attributes in C4. 5. J Artif Intell Res 4:77–90, 1996.

    MATH  Google Scholar 

  3. Hamilton, H.J., et al., RIAC: a rule induction algorithm based on approximate classification. 1996, University of Regina: International conference on engineering applications of neural networks.

  4. Ster, B. and A. Dobnikar, Neural networks in medical diagnosis: Comparison with other methods. 1996: In Proceedings of the international conference on engineering applications of neural networks. p. 427–430.

  5. Bennett, K. and J. Blue, A support vector machine approach to decision trees. 1998, in Neural Networks Proceedings,. p. 2396–2401.

  6. Nauck, D., and Kruse, R., Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med 16(2):149–169, 1999.

    Article  MathSciNet  Google Scholar 

  7. Pena-Reyes, C. A., and Sipper, M., A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 17(2):131–155, 1999.

    Article  Google Scholar 

  8. Setiono, R., Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 18(3):205–219, 2000.

    Article  Google Scholar 

  9. Goodman, D., Boggess, L., and Watkins, A., Artificial immune system classification of multiple-class problems. 2002: In Proceedings of the Artificial Neural Networks in Engineering, 2002, pp.179–183.

  10. Abonyi, J., and Szeifert, F., Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14):2195–2207, 2003.

    Article  MATH  Google Scholar 

  11. Übeyli, E. D., A mixture of experts network structure for breast cancer diagnosis. J Med Syst 29(5):569–579, 2005.

    Article  Google Scholar 

  12. Sahan, S., et al., A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput Biol Med 37(3):415–423, 2007.

    Article  Google Scholar 

  13. Ubeyli, E. D., Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33(4):1054–1062, 2007.

    Article  Google Scholar 

  14. Polat, K., and Gunes, S., Breast cancer diagnosis using least square support vector machine. Digital Signal Process 17(4):694–701, 2007.

    Article  Google Scholar 

  15. Akay, M. F., Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2):3240–3247, 2009.

    Article  Google Scholar 

  16. Übeyli, E. D., Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer. J Med Syst 33(5):353–358, 2009.

    Article  Google Scholar 

  17. Karabatak, M., and Ince, M. C., An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36(2, Part 2):3465–3469, 2009.

    Article  Google Scholar 

  18. Huang, M.-L., Hung, Y.-H., and Chen, W.-Y., Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis. J Med Syst 34(5):865–873, 2010.

    Article  Google Scholar 

  19. Marcano-Cedeño, A., J. Quintanilla-Domíngueza, and D. Andina, WBCD Breast Cancer Database Classification Applying Artificial Metaplasticity Neural Network. Expert Systems with Applications, 2011. http://dx.doi.org/10.1016/j.eswa.2011.01.167.

  20. Fan, C.-Y., et al., A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl Soft Comput 11(1):632–644, 2011.

    Article  Google Scholar 

  21. Chen, H. L., et al., A support vector machine classifier with rough set based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014–9022, 2011.

    Article  Google Scholar 

  22. Vapnik, V.N., The nature of statistical learning theory. Springer: New York, 1995.

    MATH  Google Scholar 

  23. Shawe-Taylor, J. and N. Cristianini, Kernel methods for pattern analysis. Cambridge Univ Pr, 2004.

  24. Cristianini, N. and J. Shawe-Taylor, An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge Univ Press, 2000.

  25. Cortes, C., and Vapnik, V., Support-vector networks. Mach Learn 20(3):273–297, 1995.

    MATH  Google Scholar 

  26. Osuna, E., R. Freund, and F. Girosit. Training support vector machines: an application to face detection. 1997.

  27. Joachims, T., C. Nedellec, and C. Rouveirol. Text categorization with support vector machines: learning with many relevant. Springer, 1998.

  28. John, G.H., R. Kohavi, and K. Pfleger. Irrelevant features and the subset selection problem. 1994: In Proceedings of ICML-94, 11th International Conference on Machine Learning (New Brunswick, NJ, 1994), 121–129. .

  29. Frohlich, H., Chapelle, O., and Scholkopf, B., Feature selection for support vector machines by means of genetic algorithms. IEEE Computer Society: Washington, 2003.

    Google Scholar 

  30. Hsu, C.W., C.C. Chang, and C.J. Lin, A practical guide to support vector classification. 2003, Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2003. available at http://www.csie.ntu.edu.tw/cjlin/libsvm/.

  31. Keerthi, S., Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Trans Neural Netw 13(5):1225–1229, 2002.

    Article  Google Scholar 

  32. Clerc, M., and Kennedy, J., The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73, 2002.

    Article  Google Scholar 

  33. Boser, B.E., I.M. Guyon, and V.N. Vapnik. A training algorithm for optimal margin classifiers. ACM: New York, 1992.

    Google Scholar 

  34. Vapnik, V., Statistical learning theory. NY Wiley, 1998.

  35. Sch lkopf, B., C.J.C. Burges, and A.J. Smola, Advances in kernel methods: support vector learning. The MIT press, 1998.

  36. Keerthi, S., and Lin, C., Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput 15(7):1667–1689, 2003.

    Article  MATH  Google Scholar 

  37. Kennedy, J. and R.C. Eberhart. Particle swarm optimization. in: Proceedings of the IEEE International Conference on Neural Network, vol. 4, 1995, pp. 1942–1948. 1995.

  38. Eberhart, R.C. and J. Kennedy. A new optimizer using particle swarm theory. in: Sixth international symposium on micro machine and human science, Nagoya, pp 39–43. 1995.

  39. Shi, Y. and R. Eberhart. A modified particle swarm optimizer. in Proceedings of the IEEE international conference on evolutionary computation, IEEE Press, Piscataway, NJ (1998) p. 69–73. 1998.

  40. Ratnaweera, A., Halgamuge, S., and Watson, H., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255, 2004.

    Article  Google Scholar 

  41. Eberhart, R.C. and Y. Shi. Particle swarm optimization: developments, applications and resources. 2001: in: Proceedings of 2001 Congress on evolutionary computation, vol.1 2001, pp.81–86.

  42. Shi, Y. and R.C. Eberhart. Empirical study of particle swarm optimization. 1999: Congress on evolutionary computation, Washington D.C., USA, pp 1945–1949.

  43. Kennedy, J. and R.C. Eberhart. A discrete binary version of the particle swarm algorithm. in: Proceedings of IEEE conference on systems, man and cybernetics, pp 4104–4108. 1997.

  44. Chang, C.C. and C.J. Lin, LIBSVM: a library for support vector machines. 2001, Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.

  45. Salzberg, S. L., On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Min Knowl Discov 1(3):317–328, 1997.

    Article  Google Scholar 

  46. Statnikov, A., et al., GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int J Med Inform 74(7–8):491–503, 2005.

    Article  Google Scholar 

  47. Chen, Y.-W. and C.-J. Lin, Combining SVMs with Various Feature Selection Strategies, in Feature Extraction. 2006. p. 315–324.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 60873149, 60973088, 60773099 and the National High-Tech Research and Development Plan of China under Grant Nos. 2006AA10Z245, 2006AA10A309. This work is also supported by the Open Projects of Shanghai Key Laboratory of Intelligent Information Processing in Fudan University under the Grant No. IIPL-09-007, the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) and the basic scientific research fund of Chinese Ministry of Education.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Hui-Ling Chen, Bo Yang, Gang Wang, Su-Jing Wang, Jie Liu & Da-You Liu

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Hui-Ling Chen, Bo Yang, Gang Wang, Su-Jing Wang, Jie Liu & Da-You Liu

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  1. Hui-Ling Chen
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Correspondence to Da-You Liu.

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Chen, HL., Yang, B., Wang, G. et al. Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence. J Med Syst 36, 2505–2519 (2012). https://doi.org/10.1007/s10916-011-9723-0

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