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

Yan Zhang, Chandra Kambhampati, Darryl Davis, Kevin Goode, John Cleland

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    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.
    Original languageEnglish
    Title of host publication2012 9th International Conference on Fuzzy Systems and Knowledge Discovery
    PublisherIEEE
    Pages2840 - 2844
    ISBN (Electronic)978-1-4673-0024-7
    ISBN (Print)978-1-4673-0025-4
    DOIs
    Publication statusPublished - 2012

    Keywords

    • missing value
    • imputation
    • classification
    • clinical data
    • heart failure

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  • Cite this

    Zhang, Y., Kambhampati, C., Davis, D., Goode, K., & Cleland, J. (2012). A comparative study of missing value imputation with multiclass classification for clinical heart failure data. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (pp. 2840 - 2844). IEEE. https://doi.org/10.1109/FSKD.2012.6233805