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An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data

Author

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  • Yuzhe Liu

    (Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA
    Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, PA 15260, USA)

  • Vanathi Gopalakrishnan

    (Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA
    Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
    Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
    Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA)

Abstract

Many clinical research datasets have a large percentage of missing values that directly impacts their usefulness in yielding high accuracy classifiers when used for training in supervised machine learning. While missing value imputation methods have been shown to work well with smaller percentages of missing values, their ability to impute sparse clinical research data can be problem specific. We previously attempted to learn quantitative guidelines for ordering cardiac magnetic resonance imaging during the evaluation for pediatric cardiomyopathy, but missing data significantly reduced our usable sample size. In this work, we sought to determine if increasing the usable sample size through imputation would allow us to learn better guidelines. We first review several machine learning methods for estimating missing data. Then, we apply four popular methods (mean imputation, decision tree, k-nearest neighbors, and self-organizing maps) to a clinical research dataset of pediatric patients undergoing evaluation for cardiomyopathy. Using Bayesian Rule Learning (BRL) to learn ruleset models, we compared the performance of imputation-augmented models versus unaugmented models. We found that all four imputation-augmented models performed similarly to unaugmented models. While imputation did not improve performance, it did provide evidence for the robustness of our learned models.

Suggested Citation

  • Yuzhe Liu & Vanathi Gopalakrishnan, 2017. "An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data," Data, MDPI, vol. 2(1), pages 1-15, January.
  • Handle: RePEc:gam:jdataj:v:2:y:2017:i:1:p:8-:d:88768
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    References listed on IDEAS

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    1. Geert Molenberghs & Caroline Beunckens & Cristina Sotto & Michael G. Kenward, 2008. "Every missingness not at random model has a missingness at random counterpart with equal fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 371-388, April.
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    Cited by:

    1. Sadaf Kabir & Leily Farrokhvar, 2022. "Non-linear missing data imputation for healthcare data via index-aware autoencoders," Health Care Management Science, Springer, vol. 25(3), pages 484-497, September.

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