Showing items 1 - 2 of 2

An Agile group aware process beyond CRISP-DM: A hospital data mining case study

- Sharma, Vishakha, Stranieri, Andrew, Ugon, Julien, Martin, Laura

  • Authors: Sharma, Vishakha , Stranieri, Andrew , Ugon, Julien , Martin, Laura
  • Date: 2017
  • Type: Text , Conference proceedings
  • Relation: ICCDA '17: Proceedings of the International Conference on Computer and Data Analysis May 2017 p. 109-113
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  • Description: The CRISP-DM methodology is commonly used in data analytics exercises within an organisation to provide system and structure to data mining processes. However, in providing a rigorous framework, CRISP-DM overlooks two facets of data analytics in organisational contexts; data mining exercises are far more agile and subject to change than presumed in CRISP-DM and central decisions regarding the interpretation of patterns discovered and the direction of analytics exercises are typically not made by individuals but by committees or groups within an organisation. The current study provides a case study of data mining in a hospital setting and suggests how the agile nature of an analytics exercise and the group reasoning inherent in key decisions can be accommodated within a CRISP-DM methodology.

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

- Wang, Jin, Lim, Cheepeng, Creighton, Douglas, Khorsavi, Abbas, Nahavandi, Saeid, Ugon, Julien, Vamplew, Peter, Stranieri, Andrew, Martin, Laura, Freischmidt, Anton

  • Authors: 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
  • Relation: Neural Computing and Applications Vol. 26, no. 2 (2015), p. 277-289
  • Full Text: false
  • Reviewed:
  • Description: 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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