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12-03-2020 | Original Article | Issue 9/2020

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

Journal:: International Journal of Machine Learning and Cybernetics > Issue 9/2020

Authors:: Haibo Jiang, Jianming Zhan, Bingzhen Sun, José Carlos R. Alcantud

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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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Title: An MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis
Authors:: Haibo Jiang
Jianming Zhan
Bingzhen Sun
José Carlos R. Alcantud
Publication date: 12-03-2020
DOI: https://doi.org/10.1007/s13042-020-01109-3
Publisher: Springer Berlin Heidelberg
Journal: International Journal of Machine Learning and Cybernetics
Issue 9/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X

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