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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records
Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a parti...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030272/ https://www.ncbi.nlm.nih.gov/pubmed/35455196 http://dx.doi.org/10.3390/e24040533 |
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author | Batra, Shivani Khurana, Rohan Khan, Mohammad Zubair Boulila, Wadii Koubaa, Anis Srivastava, Prakash |
author_facet | Batra, Shivani Khurana, Rohan Khan, Mohammad Zubair Boulila, Wadii Koubaa, Anis Srivastava, Prakash |
author_sort | Batra, Shivani |
collection | PubMed |
description | Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy. |
format | Online Article Text |
id | pubmed-9030272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90302722022-04-23 A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records Batra, Shivani Khurana, Rohan Khan, Mohammad Zubair Boulila, Wadii Koubaa, Anis Srivastava, Prakash Entropy (Basel) Article Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy. MDPI 2022-04-10 /pmc/articles/PMC9030272/ /pubmed/35455196 http://dx.doi.org/10.3390/e24040533 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Batra, Shivani Khurana, Rohan Khan, Mohammad Zubair Boulila, Wadii Koubaa, Anis Srivastava, Prakash A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records |
title | A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records |
title_full | A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records |
title_fullStr | A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records |
title_full_unstemmed | A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records |
title_short | A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records |
title_sort | pragmatic ensemble strategy for missing values imputation in health records |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030272/ https://www.ncbi.nlm.nih.gov/pubmed/35455196 http://dx.doi.org/10.3390/e24040533 |
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