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Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST

Objective: Among various assessment paradigms, the cardiopulmonary exercise test (CPET) provides rich evidence as part of the cardiopulmonary endurance (CPE) assessment. However, methods and strategies for interpreting CPET results are not in agreement. The purpose of this study is to validate the p...

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Autores principales: Deng, Jia, Fu, Yan, Liu, Qi, Chang, Le, Li, Haibo, Liu, Shenglin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600669/
https://www.ncbi.nlm.nih.gov/pubmed/36292227
http://dx.doi.org/10.3390/diagnostics12102538
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author Deng, Jia
Fu, Yan
Liu, Qi
Chang, Le
Li, Haibo
Liu, Shenglin
author_facet Deng, Jia
Fu, Yan
Liu, Qi
Chang, Le
Li, Haibo
Liu, Shenglin
author_sort Deng, Jia
collection PubMed
description Objective: Among various assessment paradigms, the cardiopulmonary exercise test (CPET) provides rich evidence as part of the cardiopulmonary endurance (CPE) assessment. However, methods and strategies for interpreting CPET results are not in agreement. The purpose of this study is to validate the possibility of using machine learning to evaluate CPET data for automatically classifying the CPE level of workers in high-latitude areas. Methods: A total of 120 eligible workers were selected for this cardiopulmonary exercise experiment, and the physiological data and completion of the experiment were recorded in the simulated high-latitude workplace, within which 84 sets of data were used for XGBOOST model training and36 were used for the model validation. The model performance was compared with Support Vector Machine and Random Forest. Furthermore, hyperparameter optimization was applied to the XGBOOST model by using a genetic algorithm. Results: The model was verified by the method of tenfold cross validation; the correct rate was 0.861, with a Micro-F1 Score of 0.864. Compared with RF and SVM, all data achieved a better performance. Conclusion: With a relatively small number of training samples, the GA-XGBOOST model fits well with the training set data, which can effectively evaluate the CPE level of subjects, and is expected to provide automatic CPE evaluation for selecting, training, and protecting the working population in plateau areas.
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spelling pubmed-96006692022-10-27 Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST Deng, Jia Fu, Yan Liu, Qi Chang, Le Li, Haibo Liu, Shenglin Diagnostics (Basel) Article Objective: Among various assessment paradigms, the cardiopulmonary exercise test (CPET) provides rich evidence as part of the cardiopulmonary endurance (CPE) assessment. However, methods and strategies for interpreting CPET results are not in agreement. The purpose of this study is to validate the possibility of using machine learning to evaluate CPET data for automatically classifying the CPE level of workers in high-latitude areas. Methods: A total of 120 eligible workers were selected for this cardiopulmonary exercise experiment, and the physiological data and completion of the experiment were recorded in the simulated high-latitude workplace, within which 84 sets of data were used for XGBOOST model training and36 were used for the model validation. The model performance was compared with Support Vector Machine and Random Forest. Furthermore, hyperparameter optimization was applied to the XGBOOST model by using a genetic algorithm. Results: The model was verified by the method of tenfold cross validation; the correct rate was 0.861, with a Micro-F1 Score of 0.864. Compared with RF and SVM, all data achieved a better performance. Conclusion: With a relatively small number of training samples, the GA-XGBOOST model fits well with the training set data, which can effectively evaluate the CPE level of subjects, and is expected to provide automatic CPE evaluation for selecting, training, and protecting the working population in plateau areas. MDPI 2022-10-19 /pmc/articles/PMC9600669/ /pubmed/36292227 http://dx.doi.org/10.3390/diagnostics12102538 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
Deng, Jia
Fu, Yan
Liu, Qi
Chang, Le
Li, Haibo
Liu, Shenglin
Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST
title Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST
title_full Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST
title_fullStr Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST
title_full_unstemmed Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST
title_short Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST
title_sort automatic cardiopulmonary endurance assessment: a machine learning approach based on ga-xgboost
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9600669/
https://www.ncbi.nlm.nih.gov/pubmed/36292227
http://dx.doi.org/10.3390/diagnostics12102538
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