Study on a back propogation neural network-based predictive model for prevalence of birth defect / 中华流行病学杂志
Chinese Journal of Epidemiology
;
(12): 507-509, 2007.
Article
in Chinese
| WPRIM
| ID: wpr-294303
ABSTRACT
<p><b>OBJECTIVE</b>To evaluate the value of a back propogation (BP) network on prediction of birth defect and to give clues on its prevention.</p><p><b>METHODS</b>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.</p><p><b>RESULTS</b>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%.</p><p><b>CONCLUSION</b>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.</p>
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Congenital Abnormalities
/
Epidemiology
/
Prevalence
/
Neural Networks, Computer
Type of study:
Prevalence study
/
Prognostic study
/
Risk factors
Limits:
Female
/
Humans
/
Infant, Newborn
/
Pregnancy
Language:
Chinese
Journal:
Chinese Journal of Epidemiology
Year:
2007
Type:
Article
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