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Effective Diagnosis of Diabetes with a Decision Tree-Initialised Neuro-fuzzy Approach

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Book cover Advances in Computational Intelligence Systems (UKCI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 840))

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Abstract

Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK

    Tianhua Chen & Grigoris Antoniou

  2. Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth, UK

    Changjing Shang & Qiang Shen

  3. School of Control and Computer Engineering, North China Electric Power University, Baoding, China

    Pan Su

Authors
  1. Tianhua Chen
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  2. Changjing Shang
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  3. Pan Su
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  4. Grigoris Antoniou
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  5. Qiang Shen
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Corresponding author

Correspondence to Tianhua Chen .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Ahmad Lotfi

  2. Faculty of Science and Technology, Bournemouth University, Poole, Dorset, United Kingdom

    Hamid Bouchachia

  3. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  4. School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Caroline Langensiepen

  5. College of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Martin McGinnity

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Chen, T., Shang, C., Su, P., Antoniou, G., Shen, Q. (2019). Effective Diagnosis of Diabetes with a Decision Tree-Initialised Neuro-fuzzy Approach. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_19

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