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首页> 外文会议>International conference on green communications and networks >Forecasting Incidence Seniority of Coal Workers' Pneumoconiosis Based on BP Neural Network
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Forecasting Incidence Seniority of Coal Workers' Pneumoconiosis Based on BP Neural Network

机译：基于BP神经网络的煤矿职工尘肺发病率预测。

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Applying the value of BP neural network model is discussed in the occupational prediction in order to provide evidence for pneumoconiosis prevention of dust operators. The data of patients who have been diagnosed as coal workers' pneumoconiosis were collected, and then the selected cases samples were randomly divided into three parts by the ratio of 3:1:1 to establish the BP neural network model, the fitting results of test and the forecast accuracy of the model, respectively. There was no significant difference between the model predictions and true value (P = 0.785 ＞ 0.05), and the coefficient of determination between the true value and predictive value of validation sample and stimulation sample were 0.875 and 0.859, respectively. The predicted relative error of validation sample and stimulation sample was 12.8 % and 14.8 %, respectively, both less than 20 %. The model is good to be used in analysis that predicts incidence seniority of the health of coal workers, and the predictions were reliable and were worth to be widely applied.
机译：讨论了在职业预测中应用BP神经网络模型的价值，为预防粉尘操作员尘肺病提供依据。收集被诊断为煤工尘肺的患者数据，然后按3：1：1的比例将选定的病例样本随机分为三部分，建立BP神经网络模型，进行检验的拟合结果和模型的预测准确性。模型预测值与真实值之间无显着性差异（P = 0.785＞ 0.05），验证样本与刺激样本的真实值与预测值之间的确定系数分别为0.875和0.859。验证样品和刺激样品的预测相对误差分别为12.8％和14.8％，均小于20％。该模型很好地用于预测煤工健康发生率的分析中，预测结果可靠，值得广泛应用。

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