Top

Neural Computing and Applications

Published in:

01-09-2013 | Original Article

A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA

Authors: Mustafa Serter Uzer, Onur Inan, Nihat Yılmaz

Published in: Neural Computing and Applications | Issue 3-4/2013

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Two hybrid feature selection methods (SFSP and SBSP) which are composed by combining the sequential forward selection and the sequential backward selection together with the principal component analysis developed by utilizing quadratic discriminant analysis classification algorithmic criteria so as to utilize in the diagnosis of breast cancer fast and effectively are presented in this study. The tenfold cross-validation method has been applied in the algorithm, which is utilized as criteria during the selection of the features. The dimension of the feature space for input has been decreased from 9 to 4 thanks to the selection of these two hybrid features. The Artificial Neural Networks have been used as classifier. The cross-validation method has been preferred also in the phase of this classification as in the case of the selection of the feature in order to increase the reliability of the result. The Wisconsin Breast Cancer Database obtained from the UCI has been utilized so as to determine the correctness of the system suggested. The values of the average correctness of the classification obtained by utilizing a tenfold cross-validation of the two hybrid systems developed earlier are found, respectively, as follows: for SFSP + NN, 97.57 % and for SBSP + NN, 98.57 %. SBSP + NN system has been observed that, among the studies carried out by implementing the cross-validation method for the breast cancer, the result appears to be very promising. The acquired results have revealed that this hybrid system applied by means of reducing dimension is an utilizable system in order to diagnose the diseases faster and more successfully.

previous article A class of type-2 fuzzy neural networks for nonlinear dynamical system identification

next article Rule extraction from support vector machines by genetic algorithms

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Akay MF (2009) Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 36(2):3240–3247. doi:10.1016/j.eswa.2008.01.009 CrossRef

Organization WH (2011) Cancer http://www.who.int/cancer/en/. Accessed 5 Sept 2011

Chou SM, Lee TS, Shao YE, Chen IF (2004) Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 27(1):133–142. doi:10.1016/j.eswa.2003.12.013 CrossRef

Yeh WC, Chang WW, Chung YY (2009) A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method. Expert Syst Appl 36(4):8204–8211. doi:10.1016/j.eswa.2008.10.004 CrossRef

Goodman DE, Boggess L, Watkins A (2002) Artificial immune system classification of multiple-class problems. In: Proceedings of the Artificial Neural Networks in Engineering ANNIE 02, pp 179–183

Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14):2195–2207. doi:10.1016/S0167-8655(03)00047-3 CrossRefMATH

Polat K, Gunes S (2007) Breast cancer diagnosis using least square support vector machine. Digit Signal Process 17(4):694–701. doi:10.1016/j.dsp.2006.10.008 CrossRef

Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36(2):3465–3469. doi:10.1016/j.eswa.2008.02.064 CrossRef

Chen HL, Yang B, Liu J, Liu DY (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38(7):9014–9022. doi:10.1016/j.eswa.2011.01.120 CrossRef

10.

Marcano-Cedeno A, Quintanilla-Dominguez J, Andina D (2011) WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst Appl 38(8):9573–9579. doi:10.1016/j.eswa.2011.01.167 CrossRef

11.

Xu Y, Qi Z, Wang J (2011) Breast cancer diagnosis based on a kernel orthogonal transform. Neural Comput Appl. doi:10.1007/s00521-011-0547-0

12.

Senapati MR, Mohanty AK, Dash S, Dash PK (2011) Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl. doi:10.1007/s00521-011-0670-y

13.

Ubeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33(4):1054–1062. doi:10.1016/j.eswa.2006.08.005 CrossRef

14.

Wolberg WH (1991) Wisconsin Breast Cancer Database http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.names. Accessed 2 Sept 2011

15.

Sasikala M, Kumaravel N (2005) Comparison of feature selection techniques for detection of malignant tumor in brain images. In: Indicon 2005 Proceedings:212–215

16.

Zhu ZX, Ong YS, Dash M (2007) Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybern Part B Cybern 37(1):70–76. doi:10.1109/Tsmcb.2006.883267 CrossRef

17.

Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324CrossRefMATH

18.

Bermejo P, Gamez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recognit Lett 32(5):701–711. doi:10.1016/j.patrec.2010.12.016 MathSciNetCrossRef

19.

Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature-selection. Pattern Recognit Lett 15(11):1119–1125CrossRef

20.

Ladha L, Deepa T (2011) Feature selection methods and algorithms. Int J Comput Sci Eng (IJCSE) 3(5):1787–1797

21.

Kim KS, Choi HH, Moon CS, Mun CW (2011) Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions. Curr Appl Phys 11(3):740–745. doi:10.1016/j.cap.2010.11.051 CrossRef

22.

Jolliffe IT (2002) Principal component analysis, 2nd edn. Springer, New YorkMATH

23.

Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRef

24.

Calisir D, Dogantekin E (2011) A new intelligent hepatitis diagnosis system: PCA-LSSVM. Expert Syst Appl 38(8):10705–10708. doi:10.1016/j.eswa.2011.01.014 CrossRef

25.

Sagiroglu S, Yilmaz N (2009) Web-based mobile robot platform for real-time exercises. Expert Syst Appl 36(2):3153–3166. doi:10.1016/j.eswa.2008.01.046 CrossRef

26.

Ubeyli ED, Guler I (2004) Multilayer perceptron neural networks to compute quasistatic parameters of asymmetric coplanar waveguides. Neurocomputing 62:349–365. doi:10.1016/j.neucom.2004.04.005 CrossRef

27.

Yıldırım H, Altınsoy H, Barışçı N, Ergün U, Oğur E, Hardalaç F, Güler İ (2004) Classification of the frequency of carotid stenosis with MLP and RBF neural networks in patients with coroner artery disease. J Med Syst 28(6):591–601CrossRef

28.

Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, New YorkMATH

29.

Guler I, Ubeyli ED (2005) ECG beat classifier designed by combined neural network model. Pattern Recognit 38(2):199–208. doi:10.1016/j.patcog.2004.06.009

30.

Oztemel E (2003) Yapay sinir ağları (Artificial Neural Networks). Papatya Publisher, Istanbul

31.

Lee JD (1997) Object recognition using a neural network with optimal feature extraction. Math Comput Modell 25(12):105–117CrossRefMATH

32.

Moller MF (1993) A Scaled Conjugate-Gradient Algorithm for Fast Supervised Learning. Neural Netw 6(4):525–533CrossRef

33.

Francois D, Rossi F, Wertz V, Verleysen M (2007) Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 70:1276–1288CrossRef

34.

Diamantidis NA, Karlis D, Giakoumakis EA (2000) Unsupervised stratification of cross-validation for accuracy estimation. Artificial Intell 116:1–16MathSciNetCrossRefMATH

Title: A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA
Authors: Mustafa Serter Uzer
Onur Inan
Nihat Yılmaz
Publication date: 01-09-2013
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 3-4/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0982-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 3-4/2013

Generation of artificial neural networks models in anticancer study

Artificial neural network to predict the effects of coating parameters on layer thickness of chromium carbonitride coating on pre-nitrided steels

Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network

Tracking in scale quad-rotors through adaptive augmentation

Consensus of second-order multi-agent systems with nonlinear dynamics and time delays

Rule extraction from support vector machines by genetic algorithms

Premium Partner