A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study

YANG Yutong; TIAN Qinghua; AN Qi; HAO Jianguang; WANG Jianru; WU Jiao; LI Yichun; LI Yang; WANG Qingyao; LI Yuxing; LEI Lijian; LUO Mingzhong

doi:10.16462/j.cnki.zhjbkz.2024.08.015

Volume 28 Issue 8

Aug. 2024

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2024 > 28(8): 961-968

YANG Yutong, TIAN Qinghua, AN Qi, HAO Jianguang, WANG Jianru, WU Jiao, LI Yichun, LI Yang, WANG Qingyao, LI Yuxing, LEI Lijian, LUO Mingzhong. A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(8): 961-968. doi: 10.16462/j.cnki.zhjbkz.2024.08.015

Citation:

YANG Yutong, TIAN Qinghua, AN Qi, HAO Jianguang, WANG Jianru, WU Jiao, LI Yichun, LI Yang, WANG Qingyao, LI Yuxing, LEI Lijian, LUO Mingzhong. A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(8): 961-968. doi: 10.16462/j.cnki.zhjbkz.2024.08.015

Citation:

YANG Yutong, TIAN Qinghua, AN Qi, HAO Jianguang, WANG Jianru, WU Jiao, LI Yichun, LI Yang, WANG Qingyao, LI Yuxing, LEI Lijian, LUO Mingzhong. A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(8): 961-968. doi: 10.16462/j.cnki.zhjbkz.2024.08.015

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A neural network risk prediction model of coal workers′ pneumoconiosis-a hospital-based case-control study

doi: 10.16462/j.cnki.zhjbkz.2024.08.015

YANG Yutong^{1, 2},
TIAN Qinghua^{1, 2},
AN Qi^{1, 2},
HAO Jianguang³,
WANG Jianru⁴,
WU Jiao⁴,
LI Yichun^{1, 2},
LI Yang^{1, 2},
WANG Qingyao^{1, 2},
LI Yuxing^{1, 2},
LEI Lijian^{1, 2, 5
,
,},
LUO Mingzhong^{6
,
,}

1.
Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan 030001, China
2.
MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Taiyuan 030001, China
3.
Department of Occupational Diseases and Poisoning, The Second People′s Hospital of Shanxi Province, Taiyuan 030012, China
4.
Department of Medical and Education, The Second People′s Hospital of Shanxi Province, Taiyuan 030012, China
5.
NHC Key Laboratory of Pneumoconiosis, Taiyuan 030001, China
6.
Office of the President, The Second People′s Hospital of Shanxi Province, Taiyuan 030012, China

Funds:

The "Four Batch" of Technology-Driven Medical Innovation Plan in Shanxi Province, China 2021XM43

Open Project of MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, China (MEKLCEPP/SXMU-202303)

More Information

Corresponding author: LEI Lijian, E-mail: wwdlijian@sxmu.edu.cn; LUO Mingzhong, E-mail: lmz7344@163.com<
Received Date: 2023-12-07
Rev Recd Date: 2024-05-15

Available Online: 2024-09-29

Publish Date: 2024-08-10

Abstract

Abstract

Objective This study aims to construct a high-efficiency coal workers′ pneumoconiosis (CWP) risk prediction model to promote early prevention of CWP. Methods We conducted a case-control study based on hospital records, collected case data of coal workers diagnosed with CWP and non-CWP in an occupational disease hospital in Shanxi Province from 2017 to 2022 and established a database of CWP. Random forest method was used to screen the characteristic variables. The CWP prediction model was constructed based on back propagation (BP) neural network and Logistic regression respectively, and the CWP prediction ability of the two models was evaluated by receiver operating characteristic (ROC). Results The BP neural network model demonstrated a sensitivity of 88.6%, a specificity of 87.6%, and an accuracy rate of 87.12%. Based on variable normalization importance analysis, the most influential factors for CWP prevalence in coal workers were forceful expiratory volume in 1 second/ forceful vital capacity (FEV1/FVC), working age and work type. The logistic regression model showed a sensitivity of 80.7%, a specificity of 84.1%, and an accuracy rate of 82.7%. The BP neural network model exhibited a higher area under the curve (AUC) value (AUC=0.918, 95% CI: 0.903-0.964) compared to the logistic regression model (AUC=0.802, 95% CI: 0.750-0.850), indicating superior predictive performance. Conclusions The BP neural network model provides better predictive performance compared to the logistic regression model, and applying the BP neural network to CWP prediction has higher accuracy. FEV1/FVC, working age and work type are identified as significant factors influencing the occurrence of CWP in coal workers.
- Back propagation neural network,
- Coal workers′ pneumoconiosis,
- Logistic regression,
- Prediction model

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References(24)

References

[1]	Castranova V, Vallyathan V. Silicosis and coal workers′ pneumoconiosis [J]. Environ Health Perspect, 2000, 108 Suppl 4(Suppl 4): 675-684. DOI: 10.1289/ehp.00108s4675.
[2]	Leung CC, Yu IT, Chen W. Silicosis [J]. Lancet, 2012, 379(9830): 2008-2018. DOI: 10.1016/s0140-6736(12)60235-9.
[3]	Weeks JL. The Mine Safety and Health Administration′s criterion threshold value policy increases miners′ risk of pneumoconiosis [J]. Am J Ind Med, 2006, 49(6): 492-498. DOI: 10.1002/ajim.20318.
[4]	Mukherjee AK, Bhattacharya SK, Saiyed HN. Assessment of respirable dust and its free silica contents in different Indian coalmines[J]. Ind Health, 2005, 43(2): 277-284. DOI: 10.2486/indhealth.43.277.
[5]	Xi ZL, Jiang MM, Yang JJ, et al. Experimental study on advantages of foam-Sol in coal dust control[J]. Process Saf Environ Prot, 2014, 92(6): 637-644. DOI: 10.1016/j.psep.2013.11.004.
[6]	Qi XM, Luo Y, Song MY, et al. Pneumoconiosis: current status and future prospects[J]. Chin Med J (Engl), 2021, 134(8): 898-907. DOI: 10.1097/cm9.0000000000001461.
[7]	Ge XY, Cui K, Ma HL, et al. Cost-effectiveness of comprehensive preventive measures for coal workers′ pneumoconiosis in China [J]. BMC Health Serv Res, 2022, 22(1): 266. DOI: 10.1186/s12913-022-07654-7.
[8]	Zhang L, Zhu L, Li ZH, et al. Analysis on the disease burden and its impact factors of coal worker′s pneumoconiosis inpatients [J]. J Peking Univ Health Sci, 2014, 46(2): 226-231.
[9]	Hao C, Jin N, Qiu C, et al. Balanced convolutional neural networks for pneumoconiosis detection[J]. Int J Environ Res Public Health, 2021, 18(17): 9091. DOI: 10.3390/ijerph18179091.
[10]	Moons KGM, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker [J]. Heart, 2012, 98(9): 683-690. DOI: 10.1136/heartjnl-2011-301246.
[11]	Zhang Y, Zhang Y, Liu B, et al. Prediction of the length of service at the onset of coal workers′ pneumoconiosis based on neural network [J]. Arch Environ Occup Health, 2020, 75(4): 242-250. DOI: 10.1080/19338244.2019.1644278.
[12]	Knight D, Ehrlich R, Cois A, et al. Predictors of silicosis and variation in prevalence across mines among employed gold miners in South Africa[J]. BMC Public Health, 2020, 20(1): 829. DOI: 10.1186/s12889-020-08876-2.
[13]	Han B, Liu H, Zhai G, et al. Estimates and predictions of coal workers′ pneumoconiosis cases among redeployed coal workers of the Fuxin mining industry group in China: a historical cohort study [J]. PLoS One, 2016, 11(2): e0148179. DOI: 10.1371/journal.pone.0148179.
[14]	Zhou D, Zhu D, Li N, et al. Exploration of three incidence trend prediction models based on the number of diagnosed pneumoconiosis cases in China from 2000 to 2019 [J]. J Occup Environ Med, 2021, 63(7): e440-e444. DOI: 10.1097/jom.0000000000002258.
[15]	王嵘冰, 徐红艳, 李波, 等. BP神经网络隐含层节点数确定方法研究[J]. 计算机技术与发展, 2018, 28(4): 31-35. DOI: 10.3969/j.issn.1673-629X.2018.04.007. Wang RB, Xu HY, Li B, et al. Research on method of determining hidden layer nodes in BP neural network[J]. Comput Technol Dev, 2018, 28(4): 31-35. DOI: 10.3969/j.issn.1673-629X.2018.04.007.
[16]	Li JM, Dong X, Ruan SM, et al. A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application [J]. Sci Rep, 2022, 12: 19325. DOI: 10.1038/s41598-022-21463-2.
[17]	Shao F, Huang Q, Wang C, et al. Artificial neural networking model for the prediction of early occlusion of bilateral plastic stent placement for inoperable hilar cholangiocarcinoma [J]. Surg Laparosc Endosc Percutan Tech, 2018, 28(2): e54-e58. DOI: 10.1097/sle.0000000000000502.
[18]	Liang Y, Li Q, Chen P, et al. Comparative study of back propagation artificial neural networks and logistic regression model in predicting poor prognosis after acute ischemic stroke [J]. Open Med (Wars), 2019, 14: 324-330. DOI: 10.1515/med-2019-0030.
[19]	Wu JH, Wang XH, Guo XL, et al. Forecasting incidence seniority of coal workers′ pneumoconiosis based on BP neural network[M]. Lecture Notes in Electrical Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013: 559-564. DOI: 10.1007/978-3-642-35440-3_73.
[20]	Zhang HY, Zou WM, Wu CR, et al. Influencing factors of pulmonary dysfunction in coal worker′s pneumoconiosis[J]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi, 2007, 25(1): 11-14.
[21]	Tong Y, Kong YY, Bian H, et al. Survival and disease burden trend analysis of occupational pneumoconiosis from 1963 to 2020 in Shizuishan City[J]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi, 2022, 40(5): 341-347. DOI: 10.3760/cma.j.cn121094-20210906-00439.
[22]	Han F, Chen YQ, Wu B, et al. Occupational health risk assessment of coal dust in coal industry chain[J]. Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi, 2018, 36(4): 291-294. DOI: 10.3760/cma.j.issn.1001-9391.2018.04.015.
[23]	Takigawa T, Kishimoto T, Nabe M, et al. The current state of workers′ pneumoconiosis in relationship to dusty working environments in Okayama Prefecture, Japan [J]. Acta Med Okayama, 2002, 56(6): 303-308. DOI: 10.18926/amo/31694.
[24]	Perret JL, Plush B, Lachapelle P, et al. Coal mine dust lung disease in the modern era[J]. Respirology, 2017, 22(4): 662-670. DOI: 10.1111/resp.13034.