Ismail, Amelia Ritahani and Zainal Abidin, Nadzurah and Maen, Mohd Khaled (2022) Systematic review on missing data imputation techniques with machine learning algorithms for healthcare. Journal of Robotics and Control (JRC), 3 (2). pp. 143-152. ISSN 2715-5056 E-ISSN 2715-5072
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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.
Item Type: | Article (Journal) |
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Uncontrolled Keywords: | Missing Data, Imputation, Machine Learning, Healthcare |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology |
Depositing User: | Amelia Ritahani Ismail |
Date Deposited: | 22 Jul 2022 09:02 |
Last Modified: | 22 Jul 2022 09:18 |
URI: | http://irep.iium.edu.my/id/eprint/98894 |
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