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Multiobjective evolutionary algorithm IDEA and k-means clustering for modeling multidimenional medical data based on fuzzy cognitive maps

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Abstract

The paper concerns the use of evolutionary algorithms to solve the problem of multiobjective optimization and learning of fuzzy cognitive maps (FCMs) on the basis of multidimensional medical data related to diabetes. The analyzed approach consists of two stages. The first stage is to group multidimensional medical data using k-means clustering. The second stage is automatic construction of the FCM model for each group of data based on various criteria depending on the structure and forecasting capabilities. The simulation analysis was performed with the use of the developed multiobjective Individually Directional Evolutionary Algorithm. Experiments show that the collection of fuzzy cognitive maps, in which each element is built on the basis of data for the particular group of patients, allows us to receive higher forecasting accuracy compared to the standard approaches.

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Authors and Affiliations

  1. Kielce University of Technology, Kielce, Poland

    Alexander Yastrebov & Łukasz Kubuś

  2. Kielce University of Technology, al. Tysia̧clecia Państwa Polskiego 7, 25-314, Kielce, Poland

    Katarzyna Poczeta

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  1. Alexander Yastrebov
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  2. Łukasz Kubuś
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  3. Katarzyna Poczeta
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Correspondence to Katarzyna Poczeta.

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Yastrebov, A., Kubuś, Ł. & Poczeta, K. Multiobjective evolutionary algorithm IDEA and k-means clustering for modeling multidimenional medical data based on fuzzy cognitive maps. Nat Comput 22, 601–611 (2023). https://doi.org/10.1007/s11047-022-09895-1

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