Liu, Yuzhe and Gopalakrishnan, Vanathi
(2017)
An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data.
Data, 2 (1).
p. 8.
ISSN 2306-5729
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.
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Details
| Item Type: |
Article
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| Status: |
Published |
| Creators/Authors: |
|
| Date: |
25 January 2017 |
| Date Type: |
Publication |
| Journal or Publication Title: |
Data |
| Volume: |
2 |
| Number: |
1 |
| Publisher: |
MDPI AG |
| Page Range: |
p. 8 |
| DOI or Unique Handle: |
10.3390/data2010008 |
| Schools and Programs: |
School of Medicine > Biomedical Informatics |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
missing value imputation; machine learning; decision tree imputation; k-nearest neighbors imputation; self-organizing map imputation |
| ISSN: |
2306-5729 |
| Official URL: |
http://dx.doi.org/10.3390/data2010008 |
| Funders: |
National Library of Medicine, National Institute of General Medical Sciences |
| Article Type: |
Research Article |
| Date Deposited: |
04 Feb 2021 18:13 |
| Last Modified: |
04 Feb 2021 18:13 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40220 |
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