Application of Fuzzy Logic for Generating Interpretable Pattern for Diabetes Disease in Bangladesh

  • Hasibul Kabir
  • Syed Nayeem Ridwan
  • A. T. M. Mosharof Hossain
  • Nazia Hasan Tuktuki
  • Farzan Haque
  • Farzana Afrin
  • Rashedur M RahmanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 464)


Diabetes disables body to regulate proper amount of glucose as insulin. It has impacted a vast global population. In this paper, we demonstrated a fuzzy c-means-neuro-fuzzy rule-based classifier to detect diabetic disease with an acceptable interpretability. We measured the accuracy of our implemented classifier by correctly recognizing diabetic records. Besides we measured the complexity of the classifiers by the number of selected fuzzy rules. To achieve good accuracy and interpretability, the implemented fuzzy classifier can be treated as an acceptable trade-off. At the end of the research, we compared our experiment results with the achieved results from certain medical institutions that worked on the same type of dataset which demonstrated the compactness, accuracy of the proposed approach.


Fuzzy rules Interpretable classifier Diabetes Neuro-fuzzy ANFIS FCM Bangladeshi dataset 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hasibul Kabir
    • 1
  • Syed Nayeem Ridwan
    • 1
  • A. T. M. Mosharof Hossain
    • 1
  • Nazia Hasan Tuktuki
    • 1
  • Farzan Haque
    • 1
  • Farzana Afrin
    • 1
  • Rashedur M Rahman
    • 1
    Email author
  1. 1.North South UniversityDhakaBangladesh

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