Applying data mining algorithms to inpatient dataset with missing values

Abstract

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 naïve 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.

Citation

Liu, P., El‐Darzi, E., Lei, L., Vasilakis, C., Chountas, P. and Huang, W. (2008), "Applying data mining algorithms to inpatient dataset with missing values", Journal of Enterprise Information Management, Vol. 21 No. 1, pp. 81-92. https://doi.org/10.1108/17410390810842273