Zhang Yuyuan, Zhang Yansong, Liu Bo, Meng Xiangbao
College of Mining and Safety Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China.
Arch Environ Occup Health. 2020;75(4):242-250. doi: 10.1080/19338244.2019.1644278. Epub 2019 Jul 22.
Three environmental parameters, i.e. dust concentrations, dust dispersion, and free silica content, were introduced into the traditional indices of the neural network model in order to construct a new prediction index and explore a new method for preventing the incidence of pneumoconiosis with intelligent accuracy and universality. Data of the pneumoconiosis patients from Huabei Mining Group (HBMG) of China from 1980 to 2017 were collected. SPSS22.0 was used to develop the combined models based on Back Propagation (BP) neural network model, Radial Basis Function (RBF) neural network model, and Multiple Linear Regression (MLR) model. The paired sample -test was performed between the real and predicted values. According to this model, it was predicted that 382 coal workers in HBMG were likely to suffer from pneumoconiosis in 2022 and the incidence rate was 4.48%. It is necessary to take prevention measures and transfer these workers from their current positions. In four combined models, the BP-MLR combined model achieved the optimal error parameters and the most accurate prediction. This study provided a scientific basis for effective control and prevention of the incidence of the pneumoconiosis.
将粉尘浓度、粉尘扩散度和游离二氧化硅含量这三个环境参数引入神经网络模型的传统指标中，以构建新的预测指标，并探索一种具有智能准确性和通用性的预防尘肺病发病的新方法。收集了中国华北矿业集团（HBMG）1980年至2017年尘肺病患者的数据。使用SPSS22.0基于反向传播（BP）神经网络模型、径向基函数（RBF）神经网络模型和多元线性回归（MLR）模型开发组合模型。对实际值和预测值进行配对样本检验。根据该模型预测，2022年华北矿业集团有382名煤矿工人可能患尘肺病，发病率为4.48%。有必要采取预防措施并将这些工人调离当前岗位。在四个组合模型中，BP-MLR组合模型实现了最优的误差参数和最准确的预测。本研究为有效控制和预防尘肺病的发病提供了科学依据。
