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An analysis of missing data treatment methods and their application to health care dataset

Liu, Peng and El-Darzi, Elia and Lei, Lei and Vasilakis, Christos and Chountas, Panagiotis and Huang, Wei (2005) An analysis of missing data treatment methods and their application to health care dataset. In: Li, Xue and Wang, Shuliang and Dong, Zhao Yang, (eds.) Advanced Data Mining and Applications: First International Conference, ADMA 2005, Wuhan, China, July 22-24, 2005: proceedings. Lecture notes in computer science. Lecture notes in artificial intelligence (3584). Springer, Berlin, Germany, pp. 583-590. ISBN 354027894X

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Official URL: http://dx.doi.org/10.1007/11527503_69

Abstract

It is well accepted that many real-life datasets are full of missing data. In this paper we introduce, analyze and compare several well known treatment methods for missing data handling and propose new methods based on Naive Bayesian classifier to estimate and replace missing data. We conduct extensive experiments on datasets from UCI to compare these methods. Finally we apply these models to a geriatric hospital dataset in order to assess their effectiveness on a real-life dataset.

Item Type:Book Section
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:510
Deposited On:23 Sep 2005
Last Modified:14 Oct 2009 14:29

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