文章基本信息

标题：An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data
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作者：Yuzhe Liu ; Vanathi Gopalakrishnan
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2017
卷号：2
期号：1
页码：8-22
DOI：10.3390/data2010008
出版社：MDPI Publishing
摘要：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.
关键词：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