Application of Evolutionary Fuzzy Cognitive Maps for Prediction of Pulmonary Infections

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...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2012-01, Vol.16 (1), p.143-149
Main Authors: Papageorgiou, E. I., Froelich, W.
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Algorithms
Diseases
Evolutionary learning
Female
fuzzy cognitive maps (FCMs)
Fuzzy Logic
genetic algorithms
Humans
Logistics
Lungs
Male
medical decision support
Middle Aged
Models, Theoretical
Optimization
Pneumonia - blood
Pneumonia - diagnosis
prediction
Prediction algorithms
Predictive models
Reproducibility of Results
Vectors
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Description
Summary: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.
ISSN:1089-7771
2168-2194
1558-0032
2168-2208
DOI:10.1109/TITB.2011.2175937