基于反向传播神经网络的出生缺陷患病率预测模型研究

[Study on a back propogation neural network-based predictive model for prevalence of birth defect].

作者信息

Wang Wei, Xu Wei, Zheng Ya-jun, Zhou Bao-sen

机构信息

Department of Epidemiology, China Medical University, Shenyang 110001, China.

出版信息

Zhonghua Liu Xing Bing Xue Za Zhi. 2007 May;28(5):507-9.

PMID:17877186

Abstract

OBJECTIVE

To evaluate the value of a back propogation (BP) network on prediction of birth defect and to give clues on its prevention.

METHODS

Data of birth defect in Shenyang from 1995 to 2005 were used as a training set to predict the prevalence rate of birth defect. Neural network tools box of Software MATLAB 6.5 was used to train and simulate BP Artificial Neural Network.

RESULTS

When using data of the year 1995-2003 to predict the prevalence rate of birth defect in 2004-2005, the results showed that: the fitting average error of prevalence rate was 1.34%, RNL was 0.9874, and the prediction of average error was 1.78%. Using data of the year 1995-2005 to predict the prevalence rate of birth defect in 2006-2007, the results showed that: the fitting average error was 0.33%, RNL was 0.9954, the prevalence rates of birth defect in 2006-2007 were 11.00% and 11.29%.

CONCLUSION

Compared to the conventional statistics method, BP not only showed better prediction precision, but had no limit to the type or distribution of relevant data, thus providing a powerful method in epidemiological prediction.

摘要

目的

评估反向传播（BP）网络对出生缺陷预测的价值，并为出生缺陷的预防提供线索。

方法

将沈阳市1995年至2005年的出生缺陷数据作为训练集来预测出生缺陷患病率。使用MATLAB 6.5软件的神经网络工具箱对BP人工神经网络进行训练和模拟。

结果

用1995 - 2003年的数据预测2004 - 2005年出生缺陷患病率时，结果显示：患病率拟合平均误差为1.34%，相关系数（RNL）为0.9874，预测平均误差为1.78%。用1995 - 2005年的数据预测2006 - 2007年出生缺陷患病率时，结果显示：拟合平均误差为0.33%，相关系数（RNL）为0.9954，2006 - 2007年出生缺陷患病率分别为11.00%和11.29%。