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.
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Notes
Since the number of hyperbox is an integral number, the nearest integral value is reported.
The number of the decision rules is not the same as the number of hyperboxes reported in Table 1 because the number of hyperboxes is the mean of multiple implementations.
We varied the parameters of these four classifiers and found that the accuracies fluctuated slightly.
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Acknowledgments
The authors would like to thank Ballarat Health Services, Australia, and Victorian Department of Health, Australia, for giving us access to the patient admission dataset.
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Wang, J., Lim, C.P., Creighton, D. et al. Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction. Neural Comput & Applic 26, 277–289 (2015). https://doi.org/10.1007/s00521-014-1631-z
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DOI: https://doi.org/10.1007/s00521-014-1631-z