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


CONCLUSIONS
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.
Wang
Wei
W
Department of Epidemiology, China Medical University, Shenyang 110001, China.
Xu
Wei
W
Zheng
Ya-jun
YJ
Zhou
Bao-sen
BS
D000013:Congenital Abnormalities / Q000453:epidemiology*; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D016571:Neural Networks, Computer; D011247:Pregnancy; D015995:Prevalence