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An MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis

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Abstract

In medical diagnosis, how to select an optimal medicine from some medicines with similar efficacy values to treat diseases has become common problems between doctors and patients. To solve this problem, we describe it as a multi-attribute decision-making (MADM) in a finite fuzzy covering approximation space. This paper aims to propose two pairs of covering-based variable precision fuzzy rough sets. By combining the proposed rough set model with the VIKOR method, we construct a novel method to medicine selection MADM problems in the context of medical diagnosis. A real-life case study of selecting a proper medicine to treat Alzheimer’s disease is given to demonstrate the practicality of our proposed method. Through a comparative analysis and an experimental analysis, we further explore the effectiveness and stability of the established method.

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Notes

  1. The bounded sum \(\bot _{P}(e,f)=e+f-e*f\) for each \(e, f\in [0,1]\)

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Acknowledgements

This research is partially supported by NNSFC (61866011; 11961025; 11561023; 11461025; 71571090).

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Authors and Affiliations

  1. Department of Mathematics, Hubei Minzu University, Enshi, 445000, China

    Haibo Jiang & Jianming Zhan

  2. School of Economics and Management, Xidian University, Xi’an, 710071, China

    Bingzhen Sun

  3. BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, 37007, Salamanca, Spain

    José Carlos R. Alcantud

Authors
  1. Haibo Jiang
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  2. Jianming Zhan
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  3. Bingzhen Sun
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  4. José Carlos R. Alcantud
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Correspondence to Jianming Zhan.

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Jiang, H., Zhan, J., Sun, B. et al. An MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis. Int. J. Mach. Learn. & Cyber. 11, 2181–2207 (2020). https://doi.org/10.1007/s13042-020-01109-3

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