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)

Abstract

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
• Status: Published
• Refereed: Yes
• 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 > School of Health & Wellbeing > Robertson Centre
• ISBN: 9781467300254
• Published Online: 09 July 2012