Journal of Medical Systems

, 33:353 | Cite as

Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer

  • Elif Derya ÜbeyliEmail author
Original Paper


This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer.


Adaptive neuro-fuzzy inference system (ANFIS) Fuzzy logic Breast cancer 


  1. 1.
    Miller, A. S., Blott, B. H., and Hames, T. K., Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30:449–464, 1992 doi: 10.1007/BF02457822.CrossRefGoogle Scholar
  2. 2.
    Mobley, B. A., Schechter, E., Moore, W. E., McKee, P. A., and Eichner, J. E., Predictions of coronary artery stenosis by artificial neural network. Artif. Intell. Med. 18:187–203, 2000 doi: 10.1016/S0933-3657(99)00040-8.CrossRefGoogle Scholar
  3. 3.
    Übeyli, E. D., Time-varying biomedical signals analysis with multiclass support vector machines employing Lyapunov exponents. Digit. Signal Process. 18:4646–656, 2008 doi: 10.1016/j.dsp.2007.10.001.CrossRefGoogle Scholar
  4. 4.
    Übeyli, E. D., Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals. Comput. Biol. Med. 38:5563–573, 2008 doi: 10.1016/j.compbiomed.2008.02.003.CrossRefGoogle Scholar
  5. 5.
    Übeyli, E. D., Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders. Expert Syst. Appl. 34:32201–2209, 2008 doi: 10.1016/j.eswa.2007.02.020.CrossRefGoogle Scholar
  6. 6.
    Übeyli, E. D., Comparison of different classification algorithms in clinical decision-making. Expert Syst. 24:117–31, 2007 doi: 10.1111/j.1468-0394.2007.00418.x.CrossRefGoogle Scholar
  7. 7.
    Übeyli, E. D., Detection of electrocardiogram beats using a fuzzy similarity index. Expert Syst. 24:287–96, 2007 doi: 10.1111/j.1468-0394.2007.00422.x.CrossRefGoogle Scholar
  8. 8.
    Übeyli, E. D., Combining neural network models for automated diagnostic systems. J. Med. Syst. 30:6483–488, 2006 doi: 10.1007/s10916-006-9034-z.CrossRefGoogle Scholar
  9. 9.
    Übeyli, E. D., A mixture of experts network structure for breast cancer diagnosis. J. Med. Syst. 29:5569–579, 2005 doi: 10.1007/s10916-005-6112-6.CrossRefGoogle Scholar
  10. 10.
    Kordylewski, H., Graupe, D., and Liu, K., A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Trans. Inf. Technol. Biomed. 5:3202–209, 2001 doi: 10.1109/4233.945291.CrossRefGoogle Scholar
  11. 11.
    Kwak, N., and Choi, C.-H., Input feature selection for classification problems. IEEE Trans. Neural Netw. 13:1143–159, 2002 doi: 10.1109/72.977291.CrossRefGoogle Scholar
  12. 12.
    Dubois, D., and Prade, H., An introduction to fuzzy systems. Clin. Chim. Acta. 270:3–29, 1998 doi: 10.1016/S0009-8981(97)00232-5.CrossRefGoogle Scholar
  13. 13.
    Kuncheva, L. I., and Steimann, F., Fuzzy diagnosis. Artif. Intell. Med. 16:121–128, 1999 doi: 10.1016/S0933-3657(98)00068-2.CrossRefGoogle Scholar
  14. 14.
    Nauck, D., and Kruse, R., Obtaining interpretable fuzzy classification rules from medical data. Artif. Intell. Med. 16:149–169, 1999 doi: 10.1016/S0933-3657(98)00070-0.CrossRefGoogle Scholar
  15. 15.
    Jang, J.-S. R., Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans. Neural Netw. 3:5714–723, 1992 doi: 10.1109/72.159060.CrossRefGoogle Scholar
  16. 16.
    Jang, J.-S. R., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23:3665–685, 1993 doi: 10.1109/21.256541.CrossRefMathSciNetGoogle Scholar
  17. 17.
    Usher, J., Campbell, D., Vohra, J., and Cameron, J., A fuzzy logic-controlled classifier for use in implantable cardioverter defibrillators. Pace-Pacing Clin. Electrophysiol. 22:183–186, 1999 doi: 10.1111/j.1540-8159.1999.tb00329.x.CrossRefGoogle Scholar
  18. 18.
    Belal, S. Y., Taktak, A. F. G., Nevill, A. J., Spencer, S. A., Roden, D., and Bevan, S., Automatic detection of distorted plethysmogram pulses in neonates and paediatric patients using an adaptive-network-based fuzzy inference system. Artif. Intell. Med. 24:149–165, 2002 doi: 10.1016/S0933-3657(01)00099-9.CrossRefGoogle Scholar
  19. 19.
    Virant-Klun, I., and Virant, J., Fuzzy logic alternative for analysis in the biomedical sciences. Comput. Biomed. Res. 32:305–321, 1999 doi: 10.1006/cbmr.1999.1517.CrossRefGoogle Scholar
  20. 20.
    West, D., and West, V., Model selection for a medical diagnostic decision support system: a breast cancer detection case. Artif. Intell. Med. 20:383–204, 2000 doi: 10.1016/S0933-3657(00)00063-4.CrossRefGoogle Scholar
  21. 21.
    Setiono, R., Extracting rules from pruned neural networks for breast cancer diagnosis. Artif. Intell. Med. 8:137–51, 1996 doi: 10.1016/0933-3657(95)00019-4.CrossRefGoogle Scholar
  22. 22.
    Setiono, R., Generating concise and accurate classification rules for breast cancer diagnosis. Artif. Intell. Med. 18:3205–219, 2000 doi: 10.1016/S0933-3657(99)00041-X.CrossRefGoogle Scholar
  23. 23.
    Wolberg, W. H., and Mangasarian, O. L., Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Natl. Acad. Sci. 87:9193–9196, 1990.zbMATHCrossRefGoogle Scholar
  24. 24.
    Jerez-Aragones, J. M., Gomez-Ruiz, J. A., Ramos-Jimenez, G., Munoz-Perez, J., and Alba-Conejo, E., A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif. Intell. Med. 27:145–63, 2003 doi: 10.1016/S0933-3657(02)00086-6.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringFaculty of Engineering, TOBB Ekonomi ve Teknoloji ÜniversitesiAnkaraTurkey

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