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Soft Computing

, Volume 23, Issue 14, pp 5815–5834 | Cite as

A case-base fuzzification process: diabetes diagnosis case study

  • Shaker El-Sappagh
  • Mohammed ElmogyEmail author
  • Farman Ali
  • Kyung-Sup Kwak
Methodologies and Application
  • 150 Downloads

Abstract

Medical case-based reasoning (CBR) systems require the handling of vague or imprecise data. The fuzzy set theory is particularly suitable for this purpose. This paper proposes a case-base preparation framework for CBR systems, which converts the electronic health record medical data into fuzzy CBR knowledge. It generates fuzzy case-base knowledge by suggesting a standard crisp entity–relationship data model for CBR case-base. The resulting data model is fuzzified using a proposed relational data model fuzzification methodology. The performances of this methodology and its resulting fuzzy case-base structure are evaluated. Diabetes diagnosis is used as a case study. A set of 60 real diabetic cases is used in the study. A fuzzy CBR system is implemented to check the diagnoses accuracy. It combines the resulting fuzzy case-base with a proposed fuzzy similarity measure. Experimental results indicate that the proposed fuzzy CBR method is superior to traditional CBR and other machine-learning methods. Our fuzzy CBR achieves an accuracy of 95%, a precision of 96%, a recall 97.96%, an f-measure of 96.97%, a specificity of 81.82%, and good robustness for dealing with vagueness. The resulting fuzzy case-base relational database enhances the representation of case-base knowledge, the performance of retrieval algorithms, and the querying capabilities of CBR systems.

Keywords

Case-based reasoning Diabetes diagnosis Fuzzy relational database Case retrieval Clinical decision support system 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT)-NRF-2017R1A2B2012337. The authors would like to thank Dr. Farid Badria, Prof. of Pharmacognosy, Department, and head of Liver Research Lab, Mansoura University, Egypt; and Dr. Hosam Zaghloul, Prof. at Clinical Pathology Department, Faculty of Medicine, Mansoura University, Egypt, for their efforts in this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

500_2018_3245_MOESM1_ESM.pdf (204 kb)
Supplementary material 1 (pdf 204 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shaker El-Sappagh
    • 1
    • 3
  • Mohammed Elmogy
    • 2
    Email author
  • Farman Ali
    • 3
  • Kyung-Sup Kwak
    • 3
  1. 1.Information Systems Department, Faculty of Computers and InformationMinya UniversityMinyaEgypt
  2. 2.Information Technology Department, Faculty of Computers and InformationMansoura UniversityMansouraEgypt
  3. 3.Department of Information and Communication EngineeringInha UniversityIncheonSouth Korea

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