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Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (...

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Autores principales: Li, Yupeng, Zhao, Dong, Liu, Guangjie, Liu, Yi, Bano, Yasmeen, Ibrohimov, Alisherjon, Chen, Huiling, Wu, Chengwen, Chen, Xumin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659657/
https://www.ncbi.nlm.nih.gov/pubmed/36387587
http://dx.doi.org/10.3389/fninf.2022.956423
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author Li, Yupeng
Zhao, Dong
Liu, Guangjie
Liu, Yi
Bano, Yasmeen
Ibrohimov, Alisherjon
Chen, Huiling
Wu, Chengwen
Chen, Xumin
author_facet Li, Yupeng
Zhao, Dong
Liu, Guangjie
Liu, Yi
Bano, Yasmeen
Ibrohimov, Alisherjon
Chen, Huiling
Wu, Chengwen
Chen, Xumin
author_sort Li, Yupeng
collection PubMed
description Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.
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spelling pubmed-96596572022-11-15 Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine Li, Yupeng Zhao, Dong Liu, Guangjie Liu, Yi Bano, Yasmeen Ibrohimov, Alisherjon Chen, Huiling Wu, Chengwen Chen, Xumin Front Neuroinform Neuroinformatics Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications. Frontiers Media S.A. 2022-10-31 /pmc/articles/PMC9659657/ /pubmed/36387587 http://dx.doi.org/10.3389/fninf.2022.956423 Text en Copyright © 2022 Li, Zhao, Liu, Liu, Bano, Ibrohimov, Chen, Wu and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroinformatics
Li, Yupeng
Zhao, Dong
Liu, Guangjie
Liu, Yi
Bano, Yasmeen
Ibrohimov, Alisherjon
Chen, Huiling
Wu, Chengwen
Chen, Xumin
Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
title Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
title_full Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
title_fullStr Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
title_full_unstemmed Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
title_short Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
title_sort intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine
topic Neuroinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9659657/
https://www.ncbi.nlm.nih.gov/pubmed/36387587
http://dx.doi.org/10.3389/fninf.2022.956423
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