title: Application of evolutionary fuzzy cognitive maps for prediction of pulmonary infections.
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.
authors: Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, Lamia, Greece. epapageorgiou@teilam.gr
authors: Elpiniki I
authors: EI
authors: Papageorgiou
authors: Wojciech
authors: W
authors: Froelich
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011014:Pneumonia / Q000097:blood / Q000175:diagnosis*; D015203:Reproducibility of Results