Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

Wang, Jin, Lim, Chee Peng, Creighton, Douglas, Khorsavi, Abbas, Nahavandi, Saeid, Ugon, Julien, Vamplew, Peter, Stranieri, Andrew, Martin, Laura and Freischmidt, Anton 2015, Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction, Neural computing and applications, vol. 26, no. 2, pp. 277-289, doi: 10.1007/s00521-014-1631-z.

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Title Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction
Author(s) Wang, Jin
Lim, Chee PengORCID iD for Lim, Chee Peng
Creighton, DouglasORCID iD for Creighton, Douglas
Khorsavi, AbbasORCID iD for Khorsavi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Ugon, Julien
Vamplew, Peter
Stranieri, Andrew
Martin, Laura
Freischmidt, Anton
Journal name Neural computing and applications
Volume number 26
Issue number 2
Start page 277
End page 289
Total pages 13
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-02
ISSN 0941-0643
Keyword(s) Fuzzy min–max neural network
Genetic algorithm
Patient admission prediction
Rule extraction
Summary 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.
Language eng
DOI 10.1007/s00521-014-1631-z
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Springer
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