Risk prediction of human lung ventilation dysfunction in coal miners based on machine learning

DING Yu; XUE Sheng; CHEN Qianwei; ZOU Yuanjie; MU Min; YE Dongqing

doi:10.16462/j.cnki.zhjbkz.2023.06.014

Volume 27 Issue 6

Jun. 2023

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DING Yu, XUE Sheng, CHEN Qianwei, ZOU Yuanjie, MU Min, YE Dongqing. Risk prediction of human lung ventilation dysfunction in coal miners based on machine learning[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(6): 698-704. doi: 10.16462/j.cnki.zhjbkz.2023.06.014

Citation:

DING Yu, XUE Sheng, CHEN Qianwei, ZOU Yuanjie, MU Min, YE Dongqing. Risk prediction of human lung ventilation dysfunction in coal miners based on machine learning[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(6): 698-704. doi: 10.16462/j.cnki.zhjbkz.2023.06.014

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Risk prediction of human lung ventilation dysfunction in coal miners based on machine learning

doi: 10.16462/j.cnki.zhjbkz.2023.06.014

DING Yu^{1, 4},
XUE Sheng^{1, 2, 4
,
,},
CHEN Qianwei^{3, 4},
ZOU Yuanjie^{3, 4},
MU Min^{3, 4},
YE Dongqing^{3, 4
,
,}

1.
Department of Safety Engineering, School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232000, China
2.
Joint National Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232000, China
3.
Department of Preventive Medicine, School of Public Health, Anhui University of Science and Technology, Huainan 232000, China
4.
Key Laboratory of Industrial Dust Prevention and Control, Occupational Safety and Health, Ministry of Education, Anhui University of Science and Technology, Huainan 232000, China

Funds:

The Open Research Grant of the Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining EC2021008

The Collaborative Innovation Project of Colleges and Universities of Anhui Province GXXT-2022-065

More Information

Corresponding author: XUE Sheng, E-mail: sheng.xue@aust.edu.cn; YE Dongqing, E-mail: ydqph@aust.edu.cn
Received Date: 2023-01-05
Rev Recd Date: 2023-04-23

Available Online: 2023-07-10

Publish Date: 2023-06-10

Abstract

Abstract

Objective The objective of this study was to explore the factors influencing lung ventilation dysfunction in coal miners and establish a high-accuracy predictive model using machine learning algorithms. This would aid in early detection of high-risk individuals and ensure better health safety measures for miners. Methods A total of 679 miners from a northern Shaanxi coal mine who underwent occupational health examination between April 20 and May 3, 2021, were enrolled in the study. Using unconditional multivariate logistic regression analysis and Spearman correlation test to ascertain variables, we built logistic regression (LR), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost) models. The models′ performance was evaluated on metrics such as accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and area under the receiver operating characteristic (ROC) curve. Results The accuracy rates of LR, RF, SVM and XGBoost models were 69.61%, 70.59%, 72.06% and 75.49%, respectively. The sensitivity was 61.22%, 58.16%, 60.20% and 64.29%, respectively. The specificities were 77.36%, 82.08%, 83.02% and 85.85%, respectively. The positive predictive values were 71.42%, 75.00%, 76.62% and 80.77%, respectively. The negative predictive values were 68.33%, 67.97%, 69.29% and 72.22%, respectively. F1 scores are 0.66, 0.66, 0.67 and 0.72. The areas under the ROC curve are 0.78, 0.78, 0.78 and 0.81, respectively. Among all models, the XGBoost model exhibited superior performance, and the prediction accuracy was high. Conclusions The XGBoost model proved to be an effective tool in predicting the risk of pulmonary ventilation dysfunction in coal miners. This model could form a corresponding theoretical basis for the health management of coal miners.
- Coal miners,
- Dysfunction of pulmonary ventilation,
- XGBoost,
- Predictive models

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References(16)

References

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