三种模型在矽肺发病工龄预测中的比较性研究  

Comparative study of three statistical models in prediction of working age for silicosis

作　　者：赵俊琴[1] 李建国[1] 刘惠田[1] 赵春香[1] 

机构地区：[1]河北省疾病预防控制中心,河北石家庄050021

出　　处：《中国工业医学杂志》2017年第6期418-420,共3页Chinese Journal of Industrial Medicine

基　　金：河北省卫生计生委医学科学研究重点课题;河北尘肺病流行规律与防治对策研究(20130089)

摘　　要：目的研究比较多重线性回归模型、径向基(RBF)神经网络模型、反向传播(BP)神经网络模型在矽肺发病工龄预测中的适用性。方法以2006—2015年报告的河北省壹期矽肺病例为研究对象建立数据库,并将实际发病工龄与三种模型预测的发病工龄进行配对秩和检验,计算预测值的平均相对误差和平均绝对误差。结果共获得壹期矽肺2 294例,经秩和检验,RBF神经网络模型的预测值与发病工龄的差异有统计学意义(P<0.05),其余两种模型差异均无统计学意义(P>0.05),其中BP神经网络模型的预测精度最高;开始接尘年代在矽肺发病预测中占得权重最大。结论应根据数据特点及分析需要选择适宜模型,在矽肺发病工龄预测中BP神经网络模型优于多重线性回归模型。Objective Compare the applicability of three different statistical methods as multiple linear regression model, RBF neural network model and BP neural network model in the prediction of working age for silicosis. Methods The database was established on the basis of stage-I silicosis patients reported in Hebei province between 2006 and 2015, and the rank-sum test was used for comparing the actual working age from the database and pridicted working ages by three models mentioned above, and calculate their mean absolute error and mean relative error. Results The results showed that a total number of stage-I silicosis patients were 2 294 cases, the rank sum test showed that only the difference between actual value and predicted value from RBF neural network model was statistically significant （ P〈0.05 ） , the other two differences between actual value and pre- dicted values from multiple linear regression model of BP neural network model were all no statistical significance （P〉0.05 ） , and the BP neural network model had highest prediction accuracy. Conclusion The results suggested that the selection of appro- priate model should be based on data characteristics and analytical needs, and the BP neural network model seemed better than the multiple linear regression model in the prediction of working age for silicosis.

关 键 词：壹期矽肺 接尘工龄 多重线性回归模型 RBF神经网络模型 BP神经网络模型 

分 类 号：R135.2[医药卫生—劳动卫生] R195.1[医药卫生—公共卫生与预防医学]