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

Ismail, Amelia Ritahani; Abidin, Nadzurah Zainal; Maen, Mhd Khaled

doi:10.18196/jrc.v3i2.13133

Jurnal SINTA 1

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

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

DOI: 10.18196/jrc.v3i2.13133
ISSN: 2715-5072, 2715-5056
Terbit: 2022-02-05
Penerbit: Universitas Muhammadiyah Yogyakarta
Bahasa: eng

Abstrak

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.

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International Islamic University Malaysia
Uppsala University
Universiti Malaysia Pahang Al-Sultan Abdullah
Northumbria University
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Rekan penulis	Organisasi	Artikel bersama
Nur Azri Shaharuddin	Afiliasi tidak diketahui	1
Asmarani Ahmad Puzi	International Islamic University Malaysia	1
Suryanti Awang	Universiti Malaysia Pahang Al-Sultan Abdullah	1
Ahsiah Ismail	International Islamic University Malaysia	1
Muhammad Afiq Mohd Ara	International Islamic University Malaysia	1
Roziana Ramli	Northumbria University	1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.

Metadata asli (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>
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