摘要
目的探讨自回归滑动平均混合模型(autoregressive integrated moving average,ARIMA)与误差逆传播((back propagation,BP)神经网络模型在甘肃省结核病发病率预测中的预测效果,选取合适的模型预测发病趋势.方法以甘肃省1997-2017年结核病数据为基础,建立ARIMA时间序列模型和BP神经网络模型分别预测2018-2019年的发病率,并比较两种模型的预测精度和建模效果.结果对于甘肃省2018年和2019年结核病发病率,ARIMA时间序列模型预测结果为55.1075,54.5373,MSE=92.24,MAE=7.5313,MAPE=9.26%;BP神经网络模型预测结果为62.0132,73.4460,MSE=9.6575,MAE=1.1449,MAPE=1.68%.结论 BP神经网络模型对甘肃省结核病发病率的预测效果更佳,预测得2018-2019年甘肃省结核病发病率将呈小幅上升趋势.
Objective To investigate the predictive effect of autoregressive integrated moving average (ARIMA) model and back propagation neural network (BPNN) in the prediction of tuberculosis incidence in Gansu Province,and to select appropriate models to predict the incidence. Methods Based on the data of tuberculosis in Gansu Province from 1997 to 2017,the ARIMA time series model and BP neural network model were established to predict the incidence from 2018 to 2019,and the prediction accuracy and modeling effect of the two models were compared. Results For the incidence of tuberculosis in Gansu Province in 2018 and 2019,the ARIMA model predicted results were 55.1075,54.5373,MSE = 92.24,MAE = 7.5313,MAPE = 9.26%;BP neural network model predicted results were 62.0132, 73.4460,MSE = 9.6575,MAE = 1.1449,MAPE = 1.68%. Conclusions The BP neural network model has a better predictive effect on the incidence of tuberculosis in Gansu Province,and it shows that the incidence of tuberculosis in Gansu Province will increase slightly from 2018 to 2019.
作者
杨文姣
肖俊玲
丁国武
YANG Wen-Jiao;XIAO Jun-Ling;DING Guo-wu(Institute of Social Medicine and Health Management,School of Public Health,Lanzhou University,Lanzhou 730000,China;Institute of Labor and Environmental Health,School of Public Health,Lanzhou University,Lanzhou 730000,China)
出处
《中华疾病控制杂志》
CAS
CSCD
北大核心
2019年第6期728-732,共5页
Chinese Journal of Disease Control & Prevention
