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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

Abstract

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.

Keywords

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) 

Notes

Acknowledgments

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.

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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

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