NDLI: Handling missing values in data mining - A case study of heart failure dataset

Content Provider	IEEE Xplore Digital Library
Author	Poolsawad, N. Moore, L. Kambhampati, C. Cleland, J.G.F.
Copyright Year	2012
Description	Author affiliation: Distributed Reliable Intelligent Systems Research Group (DRIS), Department of Computer Science, University of Hull, Cottingham Road, HU6 7RX, United Kingdom, (Poolsawad, N.; Moore, L.; Kambhampati, C.; Cleland, J.G.F.)
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.
Starting Page	2934
Ending Page	2938
File Size	589283
Page Count	5
File Format	PDF
ISBN	9781467300254
e-ISBN	9781467300247
DOI	10.1109/FSKD.2012.6233860
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart heart failure clinical dataset data mining Artificial neural networks Predictive models Classification algorithms Data mining classification Accuracy Prediction algorithms feature selection missing values
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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