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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 9/2020

12.03.2020 | Original Article

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

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

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 9/2020

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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.
Vorheriger Artikel Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems

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Fußnoten
1
The bounded sum \(\bot _{P}(e,f)=e+f-e*f\) for each \(e, f\in [0,1]\)
 
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Metadaten
Titel
An MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis
verfasst von
Haibo Jiang
Jianming Zhan
Bingzhen Sun
José Carlos R. Alcantud
Publikationsdatum
12.03.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 9/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01109-3

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