2021
DOI: 10.1108/dta-12-2020-0298
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A systematic review of machine learning-based missing value imputation techniques

Abstract: PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods … Show more

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Cited by 46 publications
(27 citation statements)
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“…Missing data is an inevitable and challenging issue in our retrospective study, which may lead to a biased conclusion if handled inappropriately. The K-nearest neighbors rule is an effective algorithm to impute missing data ( 34 ), although it should not be the fundamental solution. The reasons for missing data are probably because (1) the clinical significance of a series of laboratory indicators was not evidenced sufficiently as the biomarkers to predict adverse outcomes of preeclampsia.…”
Section: Discussionmentioning
confidence: 99%
“…Missing data is an inevitable and challenging issue in our retrospective study, which may lead to a biased conclusion if handled inappropriately. The K-nearest neighbors rule is an effective algorithm to impute missing data ( 34 ), although it should not be the fundamental solution. The reasons for missing data are probably because (1) the clinical significance of a series of laboratory indicators was not evidenced sufficiently as the biomarkers to predict adverse outcomes of preeclampsia.…”
Section: Discussionmentioning
confidence: 99%
“…MVI methods come in many flavors and can be classified into four categories: naïve imputation, feature-based imputation, global-based imputation and ensemble imputation [29,46] (See Supplementary…”
Section: Imputation Methodsmentioning
confidence: 99%
“…There are several practices to deal and address missing data, and techniques of imputation missing values can be discovered. One of the practices that this paper attempts to discuss is an imputation techniques through machine learning algorithms [13]- [15]. A proper method of imputing can help to improve the quality of datasets for analyzing better healthcare decision.…”
Section: Introductionmentioning
confidence: 99%