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

Neural computing & applications, 2014-04, Vol.24 (5), p.1163-1177 [Peer Reviewed Journal]

Springer-Verlag London 2013 ;2015 INIST-CNRS ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-012-1324-4

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Title:
Performance analysis of support vector machines classifiers in breast cancer mammography recognition
Author: Azar, Ahmad Taher ; El-Said, Shaimaa Ahmed
Subjects: Animal tumors. Experimental tumors ; Applied sciences ; Artificial Intelligence ; Biological and medical sciences ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Computer science; control theory; systems ; Data Mining and Knowledge Discovery ; Data processing. List processing. Character string processing ; Endocrine glands. Genital system. Mammary gland ; Exact sciences and technology ; Experimental genital and mammary tumors ; Gynecology. Andrology. Obstetrics ; Image Processing and Computer Vision ; Investigative techniques, diagnostic techniques (general aspects) ; Mammary gland diseases ; Medical sciences ; Memory organisation. Data processing ; Original Article ; Probability and Statistics in Computer Science ; Radionuclide investigations ; Software ; Tumors
Is Part Of: Neural computing & applications, 2014-04, Vol.24 (5), p.1163-1177
Description: 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.
Publisher: London: Springer London
Language: English
Identifier: ISSN: 0941-0643
EISSN: 1433-3058
DOI: 10.1007/s00521-012-1324-4
Source: Alma/SFX Local Collection
ProQuest Central

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

Springer-Verlag London 2013 ;2015 INIST-CNRS ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-012-1324-4

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