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[Study on a back propogation neural network-based predictive model for prevalence of birth defect]
Wei Wang1, Wei Xu, Ya-jun Zheng
1Department of Epidemiology, China Medical University, Shenyang 110001, China.
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.