Applying data mining algorithms to inpatient dataset with missing values

Liu, Peng, El-Darzi, Elia, Lei, Lei, Vasilakis, Christos, Chountas, Panagiotis and Huang, Wei (2008) Applying data mining algorithms to inpatient dataset with missing values. Journal of Enterprise Information Management, 21 (1). pp. 81-92. ISSN 1741-0398

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Purpose - Data preparation plays an important role in data mining as most real life data sets contained missing data. This paper aims to investigate different treatment methods for missing data. Design/methodology/approach - This paper introduces, analyses and compares well-established treatment methods for missing data and proposes new methods based on naïve Bayesian classifier. These methods have been implemented and compared using a real life geriatric hospital dataset. Findings - In the case where a large proportion of the data is missing and many attributes have missing data, treatment methods based on naive Bayesian classifier perform very well. Originality/value - This paper proposes an effective missing data treatment method and offers a viable approach to predict inpatient length of stay from a data set with many missing values.

Item Type: Article
Uncontrolled Keywords: Data analysis, health services, patients
Subjects: University of Westminster > Science and Technology > Electronics and Computer Science, School of (No longer in use)
Depositing User: Miss Nina Watts
Date Deposited: 28 Feb 2007
Last Modified: 14 Oct 2009 11:53

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