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Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis

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Advanced Data Mining and Applications (ADMA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8347))

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Abstract

Although hierarchical fuzzy neural networks (FNNs) perform with high accuracy in medical diagnosis systems, their popularity is held back from well-known disadvantage of not providing explanation. This paper presents a novel rule extraction approach to extract accurate and comprehensible fuzzy IF-THEN rules via genetic algorithm (GA) from hierarchical heterogeneous FNNs (HHFNNs). When each sub-FNNs is constructed and trained, entire HHFNNs are constructed and trained jointly through integrating all trained sub-FNNs. The proposed rule extraction approach is used to extract rule set from each concerned sub-FNNs, all extracted rule sets are then combined as one set to provide automatically exclusive explanation to diagnostic conclusion when IF part contains input features and THEN part contains diagnostic conclusions. Experimental study on diagnosing three most common and important cardiovascular diseases using hospital site-measured data demonstrates that such proposed approach exhibits satisfactory explanation capability without concerning inner structures of HHFNNs.

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Author information

Authors and Affiliations

  1. Faculty of Science and Technology, University of Macau, Avenida Padre Tomas Pereira, Taipa, MACAU SAR, China

    YuanLian Cui & MingChui Dong

Authors
  1. YuanLian Cui
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  2. MingChui Dong
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Editor information

Editors and Affiliations

  1. US Air Force Office of Scientific Research, 106-0032, Tokyo, Japan

    Hiroshi Motoda

  2. School of Computer Science and Technology, Zhejiang University, 310027, Hangzhou, China

    Zhaohui Wu

  3. Faculty of Engineering and Information Technology, University of Technology, Chippendale, 2008, Sydney, NSW, Australia

    Longbing Cao

  4. Department of Computing Science, Edmonton, University of Alberta, T6G 2E8, Canada

    Osmar Zaiane

  5. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Min Yao

  6. School of Computer Science, Fudan University, 200433, Shanghai, China

    Wei Wang

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© 2013 Springer-Verlag Berlin Heidelberg

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Cui, Y., Dong, M. (2013). Extracting Fuzzy Rules from Hierarchical Heterogeneous Neural Networks for Cardiovascular Diseases Diagnosis. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_22

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  • DOI: https://doi.org/10.1007/978-3-642-53917-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53916-9

  • Online ISBN: 978-3-642-53917-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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