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  • 杨旭,张倩.极端学习机模型在张家口市手足口病发病率预测中的应用[J].第二军医大学学报,2018,39(2):226-230    [点击复制]
  • YANG Xu,ZHANG Qian.Application of extreme learning machine model in prediction of hand-foot-and-mouth disease incidence in Zhangjiakou city[J].Acad J Sec Mil Med Univ,2018,39(2):226-230   [点击复制]
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极端学习机模型在张家口市手足口病发病率预测中的应用
杨旭1,张倩2*
0
(1. 张家口市疾病预防控制中心, 张家口 075000;
2. 衡水市疾病预防控制中心, 衡水 053000
*通信作者)
摘要:
目的 探讨极端学习机(ELM)模型在手足口病发病率预测中的应用,并与神经网络模型进行比较。方法 收集2008年5月至2017年7月张家口市手足口病月发病率资料,并组成具有111个数据的时间序列,随机选择数据集中75%的数据进行学习建模,剩余25%作为预测的检验数据,以对2种模型的预测效果进行验证。结果和结论 ELM学习的平均相对误差(MRE)为0.05,预测的MRE为0.07;神经网络学习的MRE为0.09,预测的MRE为0.12。ELM模型的学习效果和预测效果优于神经网络模型,可以提高预测的精度,具有较高的实用价值。
关键词:  手足口病  极端学习机  神经网络  发病率  预测
DOI:10.16781/j.0258-879x.2018.02.0226
投稿时间:2017-09-14修订日期:2017-10-10
基金项目:衡水市科技计划自筹经费项目(2016014001Z).
Application of extreme learning machine model in prediction of hand-foot-and-mouth disease incidence in Zhangjiakou city
YANG Xu1,ZHANG Qian2*
(1. Center for Disease Control and Prevention of Zhangjiakou, Zhangjiakou 075000, Hebei, China;
2. Center for Disease Control and Prevention of Hengshui, Hengshui 053400, Hebei, China
*Corresponding author)
Abstract:
Objective To explore the application of extreme learning machine (ELM) model in predicting the incidence of hand-foot-and-mouth disease, and to compare the difference between ELM model and neural network model. Methods The monthly incidence data of hand-foot-and-mouth disease from May 2008 to Jul. 2017 in Zhangjiakou were collected and formed a time series with 111 data. To validate and evaluate the prediction performance of the two models, 75% of the randomly selected dataset were used to train model and the remaining 25% were used as testing data for prediction. Results and conclusion The mean relative errors (MREs) of learning and prediction based on ELM model were 0.05 and 0.07, respectively. The MREs of learning and prediction based on neural network model were 0.09 and 0.12, respectively. The learning and prediction effects of ELM model are better than neural network model. It can improve the accuracy of prediction and has high application value.
Key words:  hand-foot-and-mouth disease  extreme learning machine  neural network  morbidity  prediction