Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/721
Title: Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction.
Authors: Wang, Jim
Lim, Chee Peng
Creighton, Douglas
Khorsavi, Abbas
Nahavandi, Saeid
Ugon, Julien
Vamplew, Peter
Stranieri, Andrew
Martin, Laura
Freischmidt, Anton
Issue Date: 2015
Publisher: Springer Verlag
Place of publication: Germany
Journal title: Neural Computing and Applications
Volume: 26
Start Page: 277
End Page: 289
Abstract: A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.
URI: http://hdl.handle.net/11054/721
ISSN: 1433-3058
Internal ID Number: 00706
Health Subject: PATIENT ADMISSION PREDICTION
FUZZY MIN-MAX NEURAL NETWORK
GENETIC ALGORITHM
RULE EXTRACTION
EMERGENCY DEPARTMENT
PATIENT ADMISSION
EMERGENCY SERVICE, HOSPITAL
EMERGENCY DEPARTMENT MANAGEMENT
Type: Journal Article
Article
Appears in Collections:Research Output

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