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Soft Computing Applications pp 487–500Cite as

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

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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)

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  • DOI: 10.1007/978-3-642-33941-7_43
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Author information

Authors and Affiliations

  1. Misr University for Science & Technology (MUST), 6th of October City, Egypt.

    Ahmad Taher Azar

  2. Faculty of Engineering, Zagazig University, Zagazig, Sharkia, Egypt

    Shaimaa A. El-Said

  3. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Vasile Goldis Western University of Arad, Romania, Arad

    Teodora Olariu

Authors
  1. Ahmad Taher Azar
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  2. Shaimaa A. El-Said
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  3. Valentina Emilia Balas
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  4. Teodora Olariu
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Corresponding author

Correspondence to Ahmad Taher Azar .

Editor information

Editors and Affiliations

  1. , Department of Automation & Applied Infor, Aurel Vlaicu University of Arad, Blvd. Revolutiei 77, Arad, 310130, Romania

    Valentina Emilia Balas

  2. , Institute of Intelligent Engineering, Óbuda University, Bécsi út 96/b, Budapest, 1034, Hungary

    János Fodor

  3. Dept. Measurement &, Information Systems, Technical University Budapest, Magyar tudósok körutja 2, Budapest, 1521, Hungary

    Annamária R. Várkonyi-Kóczy

  4. , Department of Informatics, University of Szeged, Arpad ter 2., Szeged, 6720, Hungary

    Joszef Dombi

  5. , School of Electrical and Information, University of South Australia, 5095, Adelaide, Australia

    Lakhmi C. Jain

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Azar, A.T., El-Said, S.A., Balas, V.E., Olariu, T. (2013). Linguistic Hedges Fuzzy Feature Selection for Differential Diagnosis of Erythemato-Squamous Diseases. In: Balas, V., Fodor, J., Várkonyi-Kóczy, A., Dombi, J., Jain, L. (eds) Soft Computing Applications. Advances in Intelligent Systems and Computing, vol 195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33941-7_43

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  • DOI: https://doi.org/10.1007/978-3-642-33941-7_43

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