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Article

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

1
School of Mechanical Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
York Region Secondary Virtual School, York Region, Markham, ON L3R 3Y3, Canada
3
Shenzhen Rehabilitation & Aiding Devices Industry Association, Shenzhen 518000, China
4
Wuhan Union Hospital, Wuhan 430022, China
*
Author to whom correspondence should be addressed.
Diagnostics 2022, 12(10), 2538; https://doi.org/10.3390/diagnostics12102538
Submission received: 13 September 2022 / Revised: 5 October 2022 / Accepted: 8 October 2022 / Published: 19 October 2022

Abstract

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.
Keywords: CPET; XG-BOOST; cardiopulmonary assessment CPET; XG-BOOST; cardiopulmonary assessment

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MDPI and ACS Style

Deng, J.; Fu, Y.; Liu, Q.; Chang, L.; Li, H.; Liu, S. Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST. Diagnostics 2022, 12, 2538. https://doi.org/10.3390/diagnostics12102538

AMA Style

Deng J, Fu Y, Liu Q, Chang L, Li H, Liu S. Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST. Diagnostics. 2022; 12(10):2538. https://doi.org/10.3390/diagnostics12102538

Chicago/Turabian Style

Deng, Jia, Yan Fu, Qi Liu, Le Chang, Haibo Li, and Shenglin Liu. 2022. "Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST" Diagnostics 12, no. 10: 2538. https://doi.org/10.3390/diagnostics12102538

APA Style

Deng, J., Fu, Y., Liu, Q., Chang, L., Li, H., & Liu, S. (2022). Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST. Diagnostics, 12(10), 2538. https://doi.org/10.3390/diagnostics12102538

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