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The Impact of Entropy Weighting Technique on MCDM-Based Rankings on Patients Using Ambiguous medical Data

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Information and Software Technologies (ICIST 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1979))

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Abstract

Multi-Criteria Decision Making (MCDM) is a method that allows to make a decision based on many different factors. Such solutions are important from a practical point of view in situations where there are many important criteria to examine. This work considers a situation in which many patients suffer from multiple symptoms, and focus should be on those most in need. For this purpose, publicly available databases related to COVID-19 symptoms were used. The proposition is composed of processing different types of samples and a combination of their numerical values. Then, it is used in selected entropy-weighted MCDM methods for returning a patient’s ranking. The proposed solution shows that this approach has great potential due to the possibility of practical use.

Supported by Rector’s mentoring project at the Silesian University of Technology.

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Authors and Affiliations

  1. Silesian University of Technology, Faculty of Applied Mathematics, Gliwice, Poland

    Antoni Jaszcz

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  1. Antoni Jaszcz
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Correspondence to Antoni Jaszcz .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Audrius Lopata

  2. Kaunas University of Technology, Kaunas, Lithuania

    Daina Gudonienė

  3. Kaunas University of Technology, Kaunas, Lithuania

    Rita Butkienė

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Jaszcz, A. (2024). The Impact of Entropy Weighting Technique on MCDM-Based Rankings on Patients Using Ambiguous medical Data. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_27

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  • DOI: https://doi.org/10.1007/978-3-031-48981-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48980-8

  • Online ISBN: 978-3-031-48981-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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