- Title
- Patient admission prediction using a pruned fuzzy min-max neural network with rule extraction
- Creator
- Wang, Jin; Lim, Cheepeng; Creighton, Douglas; Khorsavi, Abbas; Nahavandi, Saeid; Ugon, Julien; Vamplew, Peter; Stranieri, Andrew; Martin, Laura; Freischmidt, Anton
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/76585
- Identifier
- vital:7588
- Identifier
- http://www.scopus.com/inward/record.url?eid=2-s2.0-84921701056&partnerID=40&md5=9040ce7212319a1f3444a6f0a21e0c51
- Identifier
- ISSN:0941-0643
- 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.
- Publisher
- Springer-Verlag London
- Relation
- Neural Computing and Applications Vol. 26, no. 2 (2015), p. 277-289
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- Fuzzy min-max neural network; Genetic algorithm; Patient admission prediction; Rule extraction; Classification (of information); Emergency rooms; Extraction; Genetic algorithms; Hospitals; Automatic prediction; Emergency departments; Optimization method; Patient admissions; Prediction accuracy; Prediction model; Rules extraction; Forecasting; 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science
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