Effective Diagnosis of Diabetes with a Decision Tree-initialised Neuro-Fuzzy Approach

Tianhua Chen, Changjing Shang, Pan Su, Grigoris Antoniou, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 18th UK Workshop on Computational Intelligence
EditorsAhmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
PublisherSpringer, Cham
Number of pages13
ISBN (Electronic)9783319979823
ISBN (Print)9783319979816
Publication statusPublished - 12 Aug 2018
Event18th UK Workshop on Computational Intelligence - Nottingham Trent University, Nottingham, United Kingdom
Duration: 5 Sep 20187 Sep 2018
Conference number: 18
http://ukci2018.uk/ (Link to Workshop Website)

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


Workshop18th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2018
Country/TerritoryUnited Kingdom
Internet address


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