Publikacje

Abstract:

The paper concerns the use of fuzzy cognitive maps and k-means clustering to solve the problem of modeling multidimensionalmedical data. A fuzzy cognitive map is a recurrent neural network that describes the analyzed phenomenon in the form of keyconcepts and causal relationships between them. It is an effective tool for modeling decision support systems and is widely used inmedicine. The aim of this paper is to analyze the use of fuzzy cognitive maps with k-means clustering to model decision supportsystems based on multidimensional data related to Parkinson’s disease. K-means method was applied to group the data, and then aseparate fuzzy cognitive map was built for each cluster to increase forecasting accuracy. The learning process was realized with theuse of the previously developed Individually Directional Evolutionary Algorithm. The obtained results confirm that the analyzedapproach provides much better forecasting accuracy than the standard approach based on one model.

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Multidimensional medical data modeling based on fuzzy cognitive maps and k-means clustering.