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

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


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.


  1. 1.
    Holt, R.I., Hanley, N.A.: Essential Endocrinology and Diabetes, vol. 41. Wiley, Chichester (2012)Google Scholar
  2. 2.
    Temurtas, H., Yumusak, N., Temurtas, F.: A comparative study on diabetes disease diagnosis using neural networks. Expert Syst. Appl. 36(4), 8610–8615 (2009)CrossRefGoogle Scholar
  3. 3.
    Polat, K., Güneş, S., Arslan, A.: A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Expert Syst. Appl. 34(1), 482–487 (2008)CrossRefGoogle Scholar
  4. 4.
    Chen, T., Shang, C., Su, P., Shen, Q.: Induction of accurate and interpretable fuzzy rules from preliminary crisp representation. Knowl. Based Syst. 146, 152–166 (2018)CrossRefGoogle Scholar
  5. 5.
    Senge, R., Hüllermeier, E.: Fast fuzzy pattern tree learning for classification. IEEE Trans. Fuzzy Syst. 23(6), 2024–2033 (2015)CrossRefGoogle Scholar
  6. 6.
    Chen, T., Shen, Q., Su, P., Shang, C.: Fuzzy rule weight modification with particle swarm optimisation. Soft Comput. 20(8), 2923–2937 (2016)CrossRefGoogle Scholar
  7. 7.
    Berlanga, F.J., Rivera, A., del Jesús, M.J., Herrera, F.: GP-COACH: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems. Inf. Sci. 180(8), 1183–1200 (2010)CrossRefGoogle Scholar
  8. 8.
    Su, P., Shen, Q., Chen, T., Shang, C.: Ordered weighted aggregation of fuzzy similarity relations and its application to detecting water treatment plant malfunction. Eng. Appl. Artif. Intell. 66, 17–29 (2017)CrossRefGoogle Scholar
  9. 9.
    Su, P., Shang, C., Chen, T., Shen, Q.: Exploiting data reliability and fuzzy clustering for journal ranking. IEEE Trans. Fuzzy Syst. 25(5), 1306–1319 (2017)CrossRefGoogle Scholar
  10. 10.
    Zou, C., Deng, H.: Using fuzzy concept lattice for intelligent disease diagnosis. IEEE Access 5, 236–242 (2017)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Hu, Y., Xiao, F., Deng, X., Deng, Y.: A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: an application in medical diagnosis. Artif. Intell. Med. 69, 1–11 (2016)CrossRefGoogle Scholar
  12. 12.
    Feng, G.: A survey on analysis and design of model-based fuzzy control systems. IEEE Trans. Fuzzy Syst. 14(5), 676–697 (2006)CrossRefGoogle Scholar
  13. 13.
    Jang, J.-S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  14. 14.
    Knowler, W.C., Bennett, P.H., Hamman, R.F., Miller, M.: Diabetes incidence and prevalence in Pima Indians: a 19-fold greater incidence than in Rochester, Minnesota. Am. J. Epidem. 108(6), 497–505 (1978)CrossRefGoogle Scholar
  15. 15.
    Breiman, L.: Classification and Regression Trees. Routledge, New York (2017)Google Scholar
  16. 16.
    Wang, L.-X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Bache, K., Lichman, M.: UCI Machine Learning Repository (2013).
  18. 18.
    Boongoen, T., Shang, C., Iam-On, N., Shen, Q.: Extending data reliability measure to a filter approach for soft subspace clustering. IEEE Trans. Syst. Man Cybern Part B (Cybern.) 41(6), 1705–1714 (2011)CrossRefGoogle Scholar
  19. 19.
    Lukmanto, R., Irwansyah, E.: The early detection of diabetes mellitus (DM) using fuzzy hierarchical model. Proc. Comput. Sci. 59, 312–319 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tianhua Chen
    • 1
    Email author
  • 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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