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 studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

Comparison of machine learning and logistic regression as predictive models for adverse maternal and neonatal outcomes of preeclampsia: A retrospective study

“…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.…”

Dealing with missing values in proteomics data

“…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…”

Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

“…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.…”