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

Journal of Robotics and Control (JRC) · Vol. 3 No. 2 (2022) · pp. 143–152

DOI
10.18196/jrc.v3i2.13133
ISSN
2715-5072, 2715-5056
Published
2022-02-05
Publisher
Universitas Muhammadiyah Yogyakarta
Language
eng

Abstract

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends.

Keywords

Collaboration network of the authors

9 authors · 24 co-authorship links

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Co-authorOrganisationJoint articles
Asmarani Ahmad PuziAffiliation unknown1
Ahsiah IsmailAffiliation unknown1
Nur Azri ShaharuddinAffiliation unknown1
Muhammad Afiq AraAffiliation unknown1
Suryanti AwangAffiliation unknown1
Roziana RamliAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare</dc:title>
	<dc:creator>Ismail, Amelia Ritahani</dc:creator>
	<dc:creator>Abidin, Nadzurah Zainal</dc:creator>
	<dc:creator>Maen, Mhd Khaled</dc:creator>
	<dc:subject xml:lang="en-US">Review</dc:subject>
	<dc:subject xml:lang="en-US">Missing Data Imputation</dc:subject>
	<dc:subject xml:lang="en-US">Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">Healthcare</dc:subject>
	<dc:description xml:lang="en-US">Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2022-02-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/13133</dc:identifier>
	<dc:identifier>10.18196/jrc.v3i2.13133</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 3 No. 2 (2022): March; 143-152</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v3i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/13133/7111</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Amelia Ritahani Ismail, Nadzurah Zainal Abidin</dc:rights>
</oai_dc:dc>