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Application of Evolutionary Fuzzy Cognitive Maps for Prediction of Pulmonary Infections
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Abstract
In this paper, a new evolutionary-based fuzzy cognitive map (FCM) methodology is proposed
to cope with the forecasting of the patient states in the case of pulmonary infections.
The goal of the research was to improve the efficiency of the prediction. This was
succeeded with a new data fuzzification procedure for observables and optimization
of gain of transformation function using the evolutionary learning for the construction
of FCM model. The approach proposed in this paper was validated using real patient
data from internal care unit. The results emerged had less prediction errors for the
examined data records than those produced by the conventional genetic-based algorithmic
approaches.