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

  • Tianhua Chen
  • Changjing Shang
  • Pan Su
  • Grigoris Antoniou
  • Qiang Shen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tianhua Chen
    • 1
  • Changjing Shang
    • 2
  • Pan Su
    • 3
  • Grigoris Antoniou
    • 1
  • Qiang Shen
    • 2
  1. 1.Department of Computer Science, School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK
  2. 2.Department of Computer Science, Institute of Mathematics, Physics and Computer ScienceAberystwyth UniversityAberystwythUK
  3. 3.School of Control and Computer EngineeringNorth China Electric Power UniversityBaodingChina

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