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Cost Reduction in Thyroid Diagnosis: A Hybrid Model with SOM and C4.5 Decision Trees

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9490))

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Abstract

The main objective of this paper is to introduce a hybrid model of Self Organizing Maps (SOM) and C4.5, to reduce the costs while maintaining an acceptable diagnostic performance. In this hybrid model, SOM is used first to form clusters and then C4.5 trees specific to each cluster is constructed. The proposed hybrid model is tested on multiclass Thyroid Data and compared to standalone C4.5 tree. Costs were reduced by 22 %–27 % and performance results vary between 88 % and 97 % in terms of accuracy and 90 %–97 % in terms of sensitivity. Cost and performance differences between the hybrid model and standalone C4.5 found to be statistically significant according to Wilcoxon signed-rank test.

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Notes

  1. 1.

    http://archive.ics.uci.edu/ml/machine-learning-databases/thyroid-disease/ File names: allbp.data, allhyper.data and allhypo.data.

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Author information

Authors and Affiliations

  1. Computer Engineering Department, Canakkale Onsekiz Mart University, Çanakkale, Turkey

    Ahmet Cumhur Kinaci & Sait Can Yucebas

Authors
  1. Ahmet Cumhur Kinaci
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  2. Sait Can Yucebas
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Corresponding author

Correspondence to Ahmet Cumhur Kinaci .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Kinaci, A.C., Yucebas, S.C. (2015). Cost Reduction in Thyroid Diagnosis: A Hybrid Model with SOM and C4.5 Decision Trees. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_50

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  • DOI: https://doi.org/10.1007/978-3-319-26535-3_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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