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A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm

Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the ext...

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Autores principales: Almasinejad, Peyman, Golabpour, Amin, Mollakhalili Meybodi, Mohammad Reza, Mirzaie, Kamal, Khosravi, Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8519720/
https://www.ncbi.nlm.nih.gov/pubmed/34659677
http://dx.doi.org/10.1155/2021/1203726
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author Almasinejad, Peyman
Golabpour, Amin
Mollakhalili Meybodi, Mohammad Reza
Mirzaie, Kamal
Khosravi, Ahmad
author_facet Almasinejad, Peyman
Golabpour, Amin
Mollakhalili Meybodi, Mohammad Reza
Mirzaie, Kamal
Khosravi, Ahmad
author_sort Almasinejad, Peyman
collection PubMed
description Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the extent of it will determine how misinterpreted the conclusions will be. All methods of parameter estimation and prediction models are based on the assumption that the data are complete. Extensive missing data will result in false predictions and increased bias. In the present study, a novel method has been proposed for the imputation of medical missing data. The method determines what algorithm is suitable for the imputation of missing data. To do so, a multiobjective particle swarm optimization algorithm was used. The algorithm imputes the missing data in a way that if a prediction model is applied to the data, both specificity and sensitivity will be optimized. Our proposed model was evaluated using real data of gastric cancer and acute T-cell leukemia (ATLL). First, the model was then used to impute the missing data. Then, the missing data were imputed using deletion, average, expectation maximization, MICE, and missForest methods. Finally, the prediction model was applied for both imputed datasets. The accuracy of the prediction model for the first and the second imputation methods was 0.5 and 16.5, respectively. The novel imputation method was more accurate than similar algorithms like expectation maximization and MICE.
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spelling pubmed-85197202021-10-16 A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm Almasinejad, Peyman Golabpour, Amin Mollakhalili Meybodi, Mohammad Reza Mirzaie, Kamal Khosravi, Ahmad J Healthc Eng Research Article Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions. Missing data is usual in research, and the extent of it will determine how misinterpreted the conclusions will be. All methods of parameter estimation and prediction models are based on the assumption that the data are complete. Extensive missing data will result in false predictions and increased bias. In the present study, a novel method has been proposed for the imputation of medical missing data. The method determines what algorithm is suitable for the imputation of missing data. To do so, a multiobjective particle swarm optimization algorithm was used. The algorithm imputes the missing data in a way that if a prediction model is applied to the data, both specificity and sensitivity will be optimized. Our proposed model was evaluated using real data of gastric cancer and acute T-cell leukemia (ATLL). First, the model was then used to impute the missing data. Then, the missing data were imputed using deletion, average, expectation maximization, MICE, and missForest methods. Finally, the prediction model was applied for both imputed datasets. The accuracy of the prediction model for the first and the second imputation methods was 0.5 and 16.5, respectively. The novel imputation method was more accurate than similar algorithms like expectation maximization and MICE. Hindawi 2021-10-08 /pmc/articles/PMC8519720/ /pubmed/34659677 http://dx.doi.org/10.1155/2021/1203726 Text en Copyright © 2021 Peyman Almasinejad et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Almasinejad, Peyman
Golabpour, Amin
Mollakhalili Meybodi, Mohammad Reza
Mirzaie, Kamal
Khosravi, Ahmad
A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm
title A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm
title_full A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm
title_fullStr A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm
title_full_unstemmed A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm
title_short A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm
title_sort dynamic model for imputing missing medical data: a multiobjective particle swarm optimization algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8519720/
https://www.ncbi.nlm.nih.gov/pubmed/34659677
http://dx.doi.org/10.1155/2021/1203726
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