title: [Application of wavelet neural networks model to forecast incidence of syphilis].
abstract: OBJECTIVE
  To apply Wavelet Neural Networks (WNN) model to forecast incidence of Syphilis.
  
  
  METHODS
  Back Propagation Neural Network (BPNN) and WNN were developed based on the monthly incidence of Syphilis in Sichuan province from 2004 to 2008. The accuracy of forecast was compared between the two models.
  
  
  RESULTS
  In the training approximation, the mean absolute error (MAE), rooted mean square error (RMSE) and mean absolute percentage error (MAPE) were 0.0719, 0.0862 and 11.52% respectively for WNN, and 0.0892, 0.1183 and 14.87% respectively for BPNN. The three indexes for generalization of models were 0.0497, 0.0513 and 4.60% for WNN, and 0.0816, 0.1119 and 7.25% for BPNN.
  
  
  CONCLUSIONS
  WNN is a better model for short-term forecasting of Syphilis.
authors.affiliation: Department of Health Statistics, West China School of Public Health, Sichuan University, Chengdu 610041, China.
authors.forename: Xian-Feng
authors.initials: XF
authors.lastname: Zhou
authors.forename: Zi-Jian
authors.initials: ZJ
authors.lastname: Feng
authors.forename: Wei-Zhong
authors.initials: WZ
authors.lastname: Yang
authors.forename: Xiao-Song
authors.initials: XS
authors.lastname: Li
mesh_terms: D002681:China / Q000453:epidemiology; D005260:Female; D005544:Forecasting; D006801:Humans; D015994:Incidence; D008297:Male; D016571:Neural Networks, Computer; D013587:Syphilis / Q000453:epidemiology*; D058067:Wavelet Analysis