Linguistic Hedges Fuzzy Feature Selection for Differential Diagnosis of Erythemato-Squamous Diseases

  • Ahmad Taher Azar
  • Shaimaa A. El-Said
  • Valentina Emilia Balas
  • Teodora Olariu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)

Abstract

The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In diagnosing of these diseases, a biopsy is vital. However, unfortunately these diseases share many histopathological features, as well. Another difficulty for the differential diagnosis is that one disease may show the features of another disease at the beginning stage and may have the characteristic features at the following stages. In this paper, a new Feature Selection based on Linguistic Hedges Neural-Fuzzy classifier is presented for the diagnosis of erythemato-squamous diseases. The performance evaluation of this system is estimated by using four training-test partition models: 50–50%, 60–40%, 70–30% and 80–20%. The highest classification accuracy of 95.7746% was achieved for 80–20% training-test partition using 3 clusters and 18 fuzzy rules, 93.820% for 50–50% training-test partition using 3 clusters and 18 fuzzy rules, 92.5234% for 70–30% training-test partition using 5 clusters and 30 fuzzy rules, and 91.6084% for 60–40% training-test partition using 6 clusters and 36 fuzzy rules. Therefore, 80–20% training-test partition using 3 clusters and 18 fuzzy rules are the best classification accuracy with RMSE of 6.5139e-013. This research demonstrated that the proposed method can be used for reducing the dimension of feature space and can be used to obtain fast automatic diagnostic systems for other diseases.

Keywords

Erythemato-Squamous Diseases Soft Computing Takagi-Sugeno-Kang (TSK) fuzzy inference system Linguistic Hedge (LH) Feature selection (FS) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdi, M.J., Giveki, D.: Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules. Eng. Appl. Artif. Intel. (2012), doi:10.1016/j.engappai.2012.01.017Google Scholar
  2. 2.
    Barati, E., Saraee, M., Mohammadi, A., et al.: A Survey on Utilization of Data Mining Approaches for Dermatological (Skin) Diseases Prediction. Cyber Journals: Multidisciplinary Journals in Science and Technology, Journal of Selected Areas in Health Informatics (JSHI): March Edition, 1–11 (2011)Google Scholar
  3. 3.
    Cetişli, B.: Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Systems with Applications 37(8), 6093–6101 (2010a)CrossRefGoogle Scholar
  4. 4.
    Cetişli, B.: The effect of linguistic hedges on feature selection: Part 2. Expert Systems with Applications 37(8), 6102–6108 (2010b)CrossRefGoogle Scholar
  5. 5.
    Chrysostomou, K.A.: The Role of Classifiers in Feature selection Selection: Number vs Nature, PhD dissertation, School of Information Systems. Computing and mathematics, Brunel University (2008)Google Scholar
  6. 6.
    Diamantidis, N.A., Karlis, D., Giakoumakis, E.A.: Unsupervised stratification of cross-validation for accuracy estimation. Artif. Intell. 116(1-2), 1–16 (2000)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Francois, D., Rossi, F., Wertz, V.: Verleysen M Resampling methods for parameter-free and robust feature selection with mutual information. Neurocomputing 70, 1276–1288 (2007)CrossRefGoogle Scholar
  8. 8.
    Gagliardi, F.: Instance-based classifiers applied to medical databases: diagnosis and knowledge extraction. Artif. Intell. Med. 52(3), 123–139 (2011)CrossRefGoogle Scholar
  9. 9.
    Govenir, H.A., Emeksiz, N.: An expert system for the differential diagnosis of ery-themato-squamous diseases. Expert Syst. Appl. 18(1), 43–49 (2000)CrossRefGoogle Scholar
  10. 10.
    Güvenir, H.A., Demiroz, G., Ilter, N.: Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif. Intell. Med. 13(3), 147–165 (1998)CrossRefGoogle Scholar
  11. 11.
    Guyon, I., Steve, G., Nikravesh, M., Zadeh, L.A.: Feature Extraction: Foundations and Applications. Springer, Heidelberg (2006)MATHGoogle Scholar
  12. 12.
    Jang, J.S.R.: ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)CrossRefGoogle Scholar
  13. 13.
    Jang, J.S.R., Sun, C.T.: Neuro-fuzzy modeling and control. Proceedings of the IEEE 83(3), 378–406 (1995)CrossRefGoogle Scholar
  14. 14.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and soft computing. Prentice-Hall, Englewood Cliffs (1997)Google Scholar
  15. 15.
    Kabari, L.G., Bakpo, F.S.: Diagnosing skin diseases using an artificial neural network. In: Proceeding of 2nd International Conference on Adaptive Science & Technology (ICAST 2009), pp. 187–191. IEEE Press, New York (2009)Google Scholar
  16. 16.
    Karabatak, M., Ince, M.C.: A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 36(10), 12500–12505 (2009)CrossRefGoogle Scholar
  17. 17.
    Lekkas, S., Mikhailov, L.: Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artif. Intell. Med. 50(2), 117–126 (2010)CrossRefGoogle Scholar
  18. 18.
    Luukka, P.: Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 38(4), 4600–4607 (2011a)CrossRefGoogle Scholar
  19. 19.
    Luukka, P.: A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Data Sets. International Journal of Fuzzy Systems 13(3), 153–162 (2011b)Google Scholar
  20. 20.
    Luukka, P., Leppälampi, T.: Similarity classifier with generalized mean applied to medical data. Comput. Biol. Med. 36(9), 1026–1040 (2006)CrossRefGoogle Scholar
  21. 21.
    Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1993)CrossRefGoogle Scholar
  22. 22.
    Nanni, L.: An ensemble of classifiers for the diagnosis of erythemato-squamous diseases. Neurocomputing 69(7-9), 842–845 (2006)CrossRefGoogle Scholar
  23. 23.
    Ozcift, A., Gulten, A.: A Robust Multi-Class Feature Selection Strategy Based on Ro-tation Forest Ensemble Algorithm for Diagnosis of Erythemato-Squamous Diseases. J. Med. Syst. 36(2), 941–949 (2012)CrossRefGoogle Scholar
  24. 24.
    Parthiban, L., Subramanian, R.: An intelligent agent for detection of erythemato-squamous diseases using Co-active Neuro-Fuzzy Inference System and genetic algorithm. In: International Conference on Intelligent Agent & Multi-Agent Systems, IAMA, pp. 1–6. IEEE Press, New York (2009)CrossRefGoogle Scholar
  25. 25.
    Polat, K., Gunes, S.: A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications 36(2), 1587–1592 (2009)CrossRefGoogle Scholar
  26. 26.
    Polat, K., Günes, S.: The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythema-to-squamous diseases. Digital Signal Processing 16(6), 922–930 (2006)CrossRefGoogle Scholar
  27. 27.
    Revett, K., Gorunescu, F., Salem, A.B., El-Dahshan, E.S.: Evaluation of the feature space of an erythematosquamous dataset using rough sets. Ann. Univers. Craiova, Romania, Math. Comput. Sci. Ser. 36(2), 123–130 (2009)MATHGoogle Scholar
  28. 28.
    Salzberg, S.L.: On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery 1, 317–327 (1997)CrossRefGoogle Scholar
  29. 29.
    Sun, C.T., Jang, J.S.R.: A neuro-fuzzy classifier and its application. In: Proc. of IEEE Int. Conf. on Fuzzy Systems, vol. 1, pp. 94–98. IEEE Press, San Francisco (1993)Google Scholar
  30. 30.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man. Cybern. 15(1), 116–132 (1985)MATHCrossRefGoogle Scholar
  31. 31.
    Xie, J., Lei, J., Xie, W., Gao, X., Shi, Y., Liu, X.: Novel Hybrid Feature Selection Algorithms for Diagnosing Erythemato-Squamous Diseases. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds.) HIS 2012. LNCS, vol. 7231, pp. 173–185. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  32. 32.
    Xie, J., Wang, C.: Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 38(5), 5809–5815 (2011)CrossRefGoogle Scholar
  33. 33.
    Xie, J.Y., Xie, W.X., Wang, C.X., et al.: A novel hybrid feature selection method based on IFSFFS and SVM for the diagnosis of erythemato-squamous diseases. In: JMLR Workshop and Conference Proceedings. Workshop on Applications of Pattern Analysis, vol. 11, pp. 142–151. MIT Press, Windsor (2010)Google Scholar
  34. 34.
    UCI, Machine Learning Repository, http://archive.ics.uci.edu/ml/index.html (accessed on July 12, 2012)
  35. 35.
    Übeyli, E.D.: Combined neural networks for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 36(3), 5107–5112 (2009)CrossRefGoogle Scholar
  36. 36.
    Ubeyli, E.D.: Multiclass support vector machines for diagnosis of erythemato-squamous diseases. Expert Syst. Appl. 35(4), 1733–1740 (2008)CrossRefGoogle Scholar
  37. 37.
    Ubeyli, E.D., Güler, I.: Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Computers in Biology and Medicine 35(5), 147–165 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ahmad Taher Azar
    • 1
  • Shaimaa A. El-Said
    • 2
  • Valentina Emilia Balas
    • 3
  • Teodora Olariu
    • 4
  1. 1.Misr University for Science & Technology (MUST)6th of October CityEgypt.
  2. 2.Faculty of EngineeringZagazig UniversityZagazigEgypt
  3. 3.Aurel Vlaicu University of AradAradRomania
  4. 4.Vasile Goldis Western University of AradAradRomania

Personalised recommendations