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Open AccessArticle

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

by Yuzhe Liu 1,2,* and Vanathi Gopalakrishnan 1,2,3,4
1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA
2
Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
3
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
4
Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Henning Müller
Received: 24 September 2016 / Revised: 15 December 2016 / Accepted: 18 January 2017 / Published: 25 January 2017
(This article belongs to the Special Issue Biomedical Informatics)
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. View Full-Text
Keywords: missing value imputation; machine learning; decision tree imputation; k-nearest neighbors imputation; self-organizing map imputation missing value imputation; machine learning; decision tree imputation; k-nearest neighbors imputation; self-organizing map imputation
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Liu, Y.; Gopalakrishnan, V. An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data. Data 2017, 2, 8.

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