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Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of trea...

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Autores principales: Ahmed, Fatma Refaat, Alsenany, Samira Ahmed, Abdelaliem, Sally Mohammed Farghaly, Deif, Mohanad A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684522/
https://www.ncbi.nlm.nih.gov/pubmed/38017008
http://dx.doi.org/10.1038/s41598-023-47837-8
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author Ahmed, Fatma Refaat
Alsenany, Samira Ahmed
Abdelaliem, Sally Mohammed Farghaly
Deif, Mohanad A.
author_facet Ahmed, Fatma Refaat
Alsenany, Samira Ahmed
Abdelaliem, Sally Mohammed Farghaly
Deif, Mohanad A.
author_sort Ahmed, Fatma Refaat
collection PubMed
description The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.
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spelling pubmed-106845222023-11-30 Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction Ahmed, Fatma Refaat Alsenany, Samira Ahmed Abdelaliem, Sally Mohammed Farghaly Deif, Mohanad A. Sci Rep Article The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant. Nature Publishing Group UK 2023-11-27 /pmc/articles/PMC10684522/ /pubmed/38017008 http://dx.doi.org/10.1038/s41598-023-47837-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ahmed, Fatma Refaat
Alsenany, Samira Ahmed
Abdelaliem, Sally Mohammed Farghaly
Deif, Mohanad A.
Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction
title Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction
title_full Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction
title_fullStr Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction
title_full_unstemmed Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction
title_short Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction
title_sort development of a hybrid lstm with chimp optimization algorithm for the pressure ventilator prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10684522/
https://www.ncbi.nlm.nih.gov/pubmed/38017008
http://dx.doi.org/10.1038/s41598-023-47837-8
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