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Forecasting model for the incidence of hepatitis A based on artificial neural network

机译：基于人工神经网络的甲型肝炎发病率预测模型

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摘要

AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon. METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and verifying sets and the data from 1998 to 2001 were made up into the test set. STATISTICA neural network (ST NN) was used to construct, train and simulate the artificial neural network. RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlation was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9 090.21, the sum square error of the training set and testing set was 8 377.52 and 712.69, respectively, they were all less than that of ARIMA model. The corresponding value of ARIMA was 12 291.79, 8 944.95 and 3 346.84, respectively. The correlation coefficient of nonlinear regression (R_(NL)) of ANN was 0.71, while the RNL of ARIMA linear autoregression model was 0.66. CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.
机译：目的：研究人工神经网络在预测具有自回归现象的甲型肝炎发病率中的应用。方法：从辽宁省疾病预防控制中心获得1981〜2001年辽宁省甲型肝炎发病率数据。我们使用时间序列分析的自回归综合移动平均值（ARIMA）模型来确定数据中是否存在任何自回归现象。然后将入射数据转换为间隔作为网络理论输出。将1981年至1997年的数据用作训练和验证集，并将1998年至2001年的数据组成测试集。 STATISTICA神经网络（ST NN）用于构建，训练和仿真人工神经网络。结果：测试了二十四个网络，保留了七个。我们发现最好的网络具有出色的性能，其回归比率为0.73，相关系数为0.69。网络中有2个输入变量，一个是AR（1），另一个是时间。隐藏层中的单元数为3。在ARIMA时间序列分析结果中，最佳模型是一阶自回归，且没有差异和平滑度。 ANN模型的总和平方误差为9 090.21，训练集和测试集的总平方误差分别为837.52和712.69，均小于ARIMA模型。 ARIMA的相应值分别为12 291.79、8 944.95和3 346.84。 ANN的非线性回归的相关系数（R_（NL））为0.71，而ARIMA线性自回归模型的RNL为0.66。结论：人工神经网络在具有自回归现象的甲型肝炎的发病率预测方面优于传统方法。

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  • 来源
    《World Journal of Gastroenterology》 |2004年第24期|p.3579-3582|共4页
  • 作者

    Peng Guan; De-Sheng Huang; Bao-Sen Zhou;

  • 作者单位

    Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110001, Liaoning Province, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 消化系及腹部疾病;
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