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OBJECTIVE: To construct and verify the incidence prediction model of occupational coal workers′ pneumoconiosis(CWP) in coal mine workers exposed to dust(hereinafter referred to as ″dust exposure″) based on a multi-layer perceptron(MLP) neural network, and explore its application value in predicting CWP incidence. METHODS: A total of 17 023 dust exposed workers in a coal mining group in Hebei Province from 1970 to 2017 were selected as the research subjects by a typical sampling method. Among them, 839 patients were confirmed as CWP and 16 185 workers did not suffered from CWP. The MLP neural network model was established with the incidence of CWP as the target output variable, and the type of work, age, beginning year of dust exposure, observation year(i.e. incubation period) and cumulative dust exposure as the input variable. The receiver operating characteristic(ROC) curve was used to evaluate the predictive ability of the built model. The established model was used to predict the high-risk group and key monitoring group population of CWP in dust-exposed workers in the following 10 years. RESULTS: There were 44 synapses in the hidden layer of the established MLP neural network model. The area under ROC curve was 0.91. The accuracy, sensitivity and specificity of the model were 92.7%, 74.8% and 93.6%, respectively. In the validation samples, the accuracy, sensitivity and specificity were 92.1%, 70.5% and 93.2%, respectively. The MLP neural network model was used to predict 1 534 workers with high risk of CWP in the following 10 years, and the individuals were located. The number of workers in need of actively monitored was 7 599. Among them, it is predicted that the incidence of CWP in different types of dust exposed workers in the following 10 years from high to low is tunneling worker, coal miner, mixing worker and auxiliary worker(P<0.01). The earlier the dust exposure began, the higher the risk of CWP(P<0.01). CONCLUSION: The MLP neural network model based on the type of work, age, beginning year of dust exposure, incubation period and cumulative dust exposure has a good performance in predicting the incidence of CWP in coal mine dust exposure workers, and can provide a reference for early preventive management measures to prevent and cure CWP.
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<p><b>OBJECTIVE</b>The purposes of this thesis were to study the behavior about workers exposed to dust and provide scientific basis for health promotion.</p><p><b>METHODS</b>We designed a questionnaire and carry it on the 746 dust workers in the 3 representative corporations of Machinery, Ceramic, and Metallurgy Industry. All data were input into computer. And a database was established with Excel. SPSS11.5 statistical analysis software was used to analyze the influence on protecting behavioral between the application of qualifications, different jobs, training or protection, and other aspects etc.</p><p><b>RESULTS</b>The rates were 94.4% and 75.3% about the regular physical examination and requirements for protective equipment. The rate of choosing an effective way of protection was generally low (15.4%). There was significant difference for among different educational background workers (P < 0.01). The rates of choosing an effective way of protection (20.3%), the regular physical examination (98.3%) and requirements for protective equipment (86.4%) in the dust workers who participated in the training of dust protection were superior than those who did not participated in the training. There was the significant difference (P < 0.05, P < 0.01). There was the significant difference for the rate of effective way of protection, regular physical examination, and requirements for protective equipment among the different corporations (P < 0.05).</p><p><b>CONCLUSIONS</b>Dust workers' using rate about the choosing an effective way of protection was generally low in Machinery, Ceramic, and Metallurgy Industry. Those who were not educated had a lower using rate about the protection behavior, regular physical examination, and requirements for protective equipment than those educated.</p>