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A comparative study of missing value imputation with multiclass classification for clinical heart failure data

Zhang, Y.; Kambhampati, C.; Davis, D. N.; Goode, K.; Cleland, J. G. F.

Authors

Y. Zhang

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Dr Kevin Goode K.M.Goode@hull.ac.uk
Research Systems Project Manager / Business Analyst

J. G. F. Cleland



Abstract

Clinical data often contains missing values. Imputation is one of the best known schemes to overcome the drawbacks associated with missing values in data mining tasks. In this work, we compared several imputation methods and analyzed their performance when applied to different classification algorithms. A clinical heart failure data set was used in these experiments. The results showed that there is no universal imputation method that performs best for all classifiers. Some imputation-classification combinations are recommended for the processing of clinical heart failure data. © 2012 IEEE.

Publication Date 2012-05
Journal Proceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012
Peer Reviewed Peer Reviewed
Pages 2840-2844
Book Title Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
ISBN 9781467300247; 9781467300254; 9781467300230
APA6 Citation Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. (2012). A comparative study of missing value imputation with multiclass classification for clinical heart failure data. In Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on. , (2840-2844). https://doi.org/10.1109/fskd.2012.6233805
DOI https://doi.org/10.1109/fskd.2012.6233805
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