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 1741-0398Full text not available from this repository.
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 naÃ¯ve 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.
|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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