Handling Missing Values in Data Mining - A Case Study of Heart Failure Dataset

Poolsawad, N., Moore, L., Kambhampati, C. and Cleland, J.G.F. (2012) Handling Missing Values in Data Mining - A Case Study of Heart Failure Dataset. In: 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Sichuan, China, 29-31 May 2012, pp. 2934-2938. ISBN 9781467300254 (doi:10.1109/FSKD.2012.6233860)

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In this paper, we investigate the characteristics of a clinical dataset using feature selection and classification techniques to deal with missing values and develop a method to quantify numerous complexities. The research aims to find features that have high effect on mortality time frame, and to design methodologies which will cope with the following challenges: missing values, high dimensionality, and the prediction problem. The experimental results will be extended to develop prediction model for HF This paper also provides a comprehensive evaluation of a set of diverse machine learning schemes for clinical datasets.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Cleland, Professor John
Authors: Poolsawad, N., Moore, L., Kambhampati, C., and Cleland, J.G.F.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > Robertson Centre
Published Online:09 July 2012

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