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

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 method

SUBMITTER: Liu Y 

PROVIDER: S-EPMC5325161 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature