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Medical diagnosis using interval type-2 fuzzy similarity measures

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Abstract

Concerning decision support in specific disease areas, making faster and more accurate medical diagnoses has become more challenging. Due to indeterminate, inconsistent, and huge patient data, judging a specific disease may be challenging. Similarity is an effective method to enhance healthcare systems by simplifying medical diagnostic generation compared with illness profiles. This paper proposes a new medical diagnosis algorithm that uses interval type-2 fuzzy similarity measures (IT-2FSMs). This algorithm calculates the degree of similarity between varieties of components of patient data and establishes methods of clustering patients based on close distances between some of their features. The proposed approach will be evaluated on UCI Machine Learning Repository medical datasets. Comparative studies will be done between different IT-2 FSMs (Cherif, Jaccard, Bustince, Mitchell, Gorzalczany, Zeng and Li) to demonstrate the capability of IT-2FSMs to make quick medical diagnoses, even with different noise levels. The proposed IT-2FSMs are utilized as a clustering method and compared against existing clustering algorithms such as type-2 fuzzy c-means, cluster forest, bagged clustering, evidence accumulation, and random projection. The IT-2FSMs demonstrate a pertinent classification accuracy comparable to the other algorithms, as assessed by the clustering quality parameter R.

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Availability of data and materials

The datasets are available through the following links: https://archive.ics.uci.edu/dataset/161/mammographic+mass; https://archive.ics.uci.edu/dataset/145/statlog+heart.

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Acknowledgements

This research project was funded by the Deanship of Scientific Research, Princess Nourah bint Abdulrahman University, through the Program of Research Project Funding after Publication (Grant No. 44- PRFA-P- 82).

Funding

Program of Research Project Funding after Publication (Grant No. 44- PRFA-P- 82).

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Authors and Affiliations

  1. REGIM-Lab.: REsearch Groups in Intelligent Machines, National School of Engineers of Sfax (ENIS), University of Sfax, Sfax, 3038, Tunisia

    Sahar Cherif & Hamdi Kchaou

  2. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riadh, 11671, Saudi Arabia

    Amel Ksibi & Ghadah Aldehim

Authors
  1. Sahar Cherif
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  3. Amel Ksibi
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Correspondence to Amel Ksibi.

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Cherif, S., Kchaou, H., Ksibi, A. et al. Medical diagnosis using interval type-2 fuzzy similarity measures. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04485-5

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