Neural Computing and Applications

, Volume 24, Issue 5, pp 1163–1177 | Cite as

Performance analysis of support vector machines classifiers in breast cancer mammography recognition

  • Ahmad Taher AzarEmail author
  • Shaimaa Ahmed El-Said
Original Article


Support vector machine (SVM) is a supervised machine learning approach that was recognized as a statistical learning apotheosis for the small-sample database. SVM has shown its excellent learning and generalization ability and has been extensively employed in many areas. This paper presents a performance analysis of six types of SVMs for the diagnosis of the classical Wisconsin breast cancer problem from a statistical point of view. The classification performance of standard SVM (St-SVM) is analyzed and compared with those of the other modified classifiers such as proximal support vector machine (PSVM) classifiers, Lagrangian support vector machines (LSVM), finite Newton method for Lagrangian support vector machine (NSVM), Linear programming support vector machines (LPSVM), and smooth support vector machine (SSVM). The experimental results reveal that these SVM classifiers achieve very fast, simple, and efficient breast cancer diagnosis. The training results indicated that LSVM has the lowest accuracy of 95.6107 %, while St-SVM performed better than other methods for all performance indices (accuracy = 97.71 %) and is closely followed by LPSVM (accuracy = 97.3282). However, in the validation phase, the overall accuracies of LPSVM achieved 97.1429 %, which was superior to LSVM (95.4286 %), SSVM (96.5714 %), PSVM (96 %), NSVM (96.5714 %), and St-SVM (94.86 %). Value of ROC and MCC for LPSVM achieved 0.9938 and 0.9369, respectively, which outperformed other classifiers. The results strongly suggest that LPSVM can aid in the diagnosis of breast cancer.


Soft computing Breast cancer diagnosis Proximal support vector machine (PSVM) Lagrangian support vector machines (LSVM) Finite Newton method for Lagrangian support vector machine (NSVM) Linear programming support vector machines (LPSVM) Smooth support vector machine (SSVM) 



I would like to highly appreciate and gratefully acknowledge, Phillip H. Sherrod [50], software developer and consultant on predictive modeling, for his support and consultation during modeling process.


  1. 1.
    Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14):2195–2207CrossRefzbMATHGoogle Scholar
  2. 2.
    Akay MF (2009) Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2):3240–3247CrossRefGoogle Scholar
  3. 3.
    Bishop C (1997) Neural networks for pattern recognition. Clarendon Press, OxfordGoogle Scholar
  4. 4.
    Blanz V, Scholkopf B, Bulthoff H et al (1996) Comparison of view–based object recognition algorithms using realistic 3d models. In: von der Malsburg C, von Seelen W, Vorbruggen JC, Sendhoff B (eds) Artificial Neural Networks—ICANN’96, Springer Lecture Notes in Computer Science, Berlin, vol 1112, pp 251–256Google Scholar
  5. 5.
    Boyle P, Levin B (2008) World Cancer report 2008. International Agency for Research on Cancer, LyonGoogle Scholar
  6. 6.
    Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167CrossRefGoogle Scholar
  7. 7.
    Burges CJC, Scholkopf B (1997) Improving the accuracy and speed of support vector learning machines. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems 9. MIT Press, Cambridge, pp 375–381Google Scholar
  8. 8.
    Cedeño AM, Domíngueza JQ, Andina D (2011) WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst Appl 38(8):9573–9579CrossRefGoogle Scholar
  9. 9.
    Chang RF, Wu WJ, Moon WK et al (2003) Support vector machines for diagnosis of breast tumors on US images. Acad Radiol 10(2):189–197CrossRefGoogle Scholar
  10. 10.
    Chen HL, Yanga B, Liua J, Liu DY (2011) A support vector machine classifier with rough set based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014–9022CrossRefGoogle Scholar
  11. 11.
    Chen HL, Yang B, Wang G et al (2011) Support vector machine based diagnostic system for breast cancer using swarm intelligence. J Med Syst. doi: 10.1007/s10916-011-9723-0 Google Scholar
  12. 12.
    Cortes C, Vapnik V (1995) Support vector network. Mach Learn 20:273–297zbMATHGoogle Scholar
  13. 13.
    Cristianini N, Taylor JS (2000) An introduction to support Vector Machines: and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  14. 14.
    Evgeniou T, Pontil M, Poggio T (2000) Regularization networks and support vector machines. In: Bartlett P, Scholkopf B, Schuurmans D, Smola AJ (eds) Advances in large margin classifiers. MIT Press, Cambridge, pp 171–203Google Scholar
  15. 15.
    Fan CY, Changb PC, Linb JJ, Hsieh JC (2011) A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification. Appl Soft Comput 11(1):632–644CrossRefGoogle Scholar
  16. 16.
    Francois D, Rossi F, Wertz V, Verleysen M (2007) Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 70:1276–1288CrossRefGoogle Scholar
  17. 17.
    Fung G, Mangasarian OL (2004) A feature selection Newton method for support vector machine classification. Comput Optim Appl 28(2):185–202CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Fung G, Mangasarian OL (2003) Finite {N}ewton method for {L}agrangian support vector machine classification. Neurocomputing 55(1–2):39–55CrossRefGoogle Scholar
  19. 19.
    Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. Proceedings of KDD’01 seventh ACM SIGKDD international conference on Knowledge Discovery and Data Mining, San Francisco, pp 77–86. ISBN: 1-58113-391-X. doi: 10.1145/502512.502527
  20. 20.
    Goodman D, Boggess L, Watkins A (2002) Artificial immune system classification of multiple-class problems. In: Dagli CH, Buczak AL, Ghosh J, Ersoy O, Kercel SW (eds) Intell Eng Syst Artif Neural Net, vol 12, pp 179–184Google Scholar
  21. 21.
    Gunn SR (1998) Support vector machines for classification and regression. Technical Report, Faculty of Engineering, University of SouthamptonGoogle Scholar
  22. 22.
    Hamilton HJ, Shan N, Cerone N (1996) RIAC: a rule induction algorithm based on approximate classification. Technical Report CS 96-06, University of Regina. ISBN 0-7731-0321-XGoogle Scholar
  23. 23.
    Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan UniversityGoogle Scholar
  24. 24.
    Huang ML, Hung YH, Chen WY (2010) Neural network classifier with entropy based feature selection on breast cancer diagnosis. J Med Syst 34(5):865–873CrossRefGoogle Scholar
  25. 25.
    Joachims T, Nedellec C, Rouveirol C (1998) Text categorization with support vector machines: learning with many relevant. Springer, Springer-Verlag GmbH, BerlinGoogle Scholar
  26. 26.
    Joachims T (1998) SVM light.
  27. 27.
    Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Exp Syst Appl 36(2, Part 2):3465–3469CrossRefGoogle Scholar
  28. 28.
    Kerekes J (2008) Receiver operating characteristic curve confidence intervals and regions. IEEE Geosci Remote Sens Lett 5(2):251–255CrossRefGoogle Scholar
  29. 29.
    Lee YJ, Mangasarian OL (2001) {SSVM}: a smooth support vector machine. Comput Optim Appl 20:5–22CrossRefzbMATHMathSciNetGoogle Scholar
  30. 30.
    Liu HX, Zhang RS, Luan F et al (2003) Diagnosing breast cancer based on support vector machines. J Chem Inf Comput Sci 43(3):900–907CrossRefGoogle Scholar
  31. 31.
    Mangasarian OL, Setiono R, Wolberg WH (1990) Pattern recognition via linear programming: theory and application to medical diagnosis. Proceedings of the workshop on large-scale numerical optimization, SIAM, Philadelphia, pp 22–31Google Scholar
  32. 32.
    Mangasarian OL, Musicant DR (2000) Lagrangian Support Vector Machine Classification. Tec. Report, Data Mining Institute, Computer Sciences Department, University of WisconsinGoogle Scholar
  33. 33.
    Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Networks 10:1032–1037CrossRefGoogle Scholar
  34. 34.
    Mangasarian OL (2000) Generalized support vector machines. In: Smola A, Bartlett P, Scholkopf B, Schuurmans D (eds) Advances in large margin classifiers. MIT Press, Cambridge, pp 135–146Google Scholar
  35. 35.
    McAree B, O’Donnell ME, Spence A et al (2010) Breast cancer in women under 40 years of age: a series of 57 cases from Northern Ireland. Breast 19(2):97–104CrossRefGoogle Scholar
  36. 36.
    Mitchell T (1997) Machine learning. The McGraw-Hill Companies, Inc., New YorkzbMATHGoogle Scholar
  37. 37.
    Nauck D, Kruse R (1999) Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med 16(2):149–169CrossRefMathSciNetGoogle Scholar
  38. 38.
    NCSS (2012) Statistical and power analysis software. Accessed in April 2012
  39. 39.
    Osuna E, Freund R, Girosit F (1997) Training support vector machines: an application to face detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Jun 17–19, pp 130–136Google Scholar
  40. 40.
    Park SH, Goo JM, Jo CH (2004) Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J Radiol 5(1):11–18CrossRefGoogle Scholar
  41. 41.
    Pena-Reyes CA, Sipper M (1999) A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 17(2):131–155CrossRefGoogle Scholar
  42. 42.
    Polat K, Gunes S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Process 17(4):694–701CrossRefGoogle Scholar
  43. 43.
    Platt J (1998) Sequential minimal optimization: A fast algorithm for training support vector machines. Technical Report MSR-TR-98-14Google Scholar
  44. 44.
    Quinlan J (1996) Improved use of continuous attributes in C4. 5. J Artif Intell Res 4:77–90zbMATHGoogle Scholar
  45. 45.
    Sahan S, Polat K, Kodaz H, Günes S (2007) A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Comput Biol Med 37(3):415–423CrossRefGoogle Scholar
  46. 46.
    Schmidt M (1996) Identifying speaker with support vector networks. Interface’96 Proceedings, SydneyGoogle Scholar
  47. 47.
    Scholkopf B, Burges C, Vapnik V (1995) Extracting support data for a given task. In: Fayyad UM, Uthurusamy R (eds) Proceedings, first international conference on knowledge discovery & data mining. AAAI Press, Menlo ParkGoogle Scholar
  48. 48.
    Scholkopf B, Burges C, Vapnik V (1996) Incorporating invariances in support vector learning machines. In: von der Malsburg C, von Seelen W, Vorbruggen JC, Sendhoff B (eds) Artificial neural networks- ICANN’96, vol 1112. Springer Lecture Notes in Computer Science, Berlin, pp 47–52Google Scholar
  49. 49.
    Setiono R (2000) Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 18(3):205–219CrossRefGoogle Scholar
  50. 50.
    Sherrod PH (2011) DTREG predictive modeling software.
  51. 51.
    Ster B, Dobnikar A (1996) Neural networks in medical diagnosis: comparison with other methods. Proceedings of the international conference on engineering applications of neural networks, pp 427–430Google Scholar
  52. 52.
    Taylor JS, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  53. 53.
    Übeyli ED (2005) A mixture of experts network structure for breast cancer diagnosis. J Med Syst 29(5):569–579CrossRefGoogle Scholar
  54. 54.
    Übeyli ED (2009) Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer. J Med Syst 33(5):353–358CrossRefGoogle Scholar
  55. 55.
    Ubeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33(4):1054–1062CrossRefGoogle Scholar
  56. 56.
    UCI (2012) Machine learning repository. Accessed on 10 Aug 2012
  57. 57.
    Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefzbMATHGoogle Scholar
  58. 58.
    Vapnik VN (1999) The nature of statistical learning theory, 2nd edn. New York, SpringerGoogle Scholar
  59. 59.
    Vapnik V, Golowich S, Smola A (1997) Support vector method for function approximation, regression estimation, and signal processing. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems 9. Cambridge, MIT Press, pp 281–287Google Scholar
  60. 60.
    Wolberg WH, Mangasarian OL (1990) Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc Natl Acad Sci 87:9193–9196CrossRefzbMATHGoogle Scholar
  61. 61.
    Yuan Q, Cai C, Xiao H et al (2007) Diagnosis of breast tumours and evaluation of prognostic risk by using machine learning approaches. Commun Comput Inform Sci 2:1250–1260CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Faculty of EngineeringMisr University for Science and Technology (MUST)6th of October CityEgypt
  2. 2.Electronics and Communications Department, Faculty of EngineeringZagazig UniversityZagazig, SharkiaEgypt

Personalised recommendations