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. 2022 Oct 19;12(10):2538.
doi: 10.3390/diagnostics12102538.

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

Jia Deng  1 , Yan Fu  1 , Qi Liu  1 , Le Chang  2 , Haibo Li  3 , Shenglin Liu  4
Affiliations

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

Jia Deng et al. Diagnostics (Basel). .

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.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Illustration of cardiopulmonary exercise testing.
Figure 2
Figure 2
Schema of the study’s design.
Figure 3
Figure 3
Pseudocodes of the XGBOOST algorithm.
Figure 4
Figure 4
Optimization Flow Chart of Genetic Algorithm.
Figure 5
Figure 5
(a) Confusion matrix predicted by XGBOOST; (b) Hierarchical classification.
Figure 6
Figure 6
Crossover Operator.
Figure 7
Figure 7
Mutation Operator.
Figure 8
Figure 8
Iteration diagram of genetic algorithm.
Figure 9
Figure 9
SHAP value of each feature for each sample.
Figure 10
Figure 10
Importance of each feature.
Figure 11
Figure 11
SHAP feature dependency graph examples of important indicators.
See this image and copyright information in PMC

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