title: [Study on the application of Back-Propagation Artificial Neural Network used the model in predicting preterm birth].
abstract: OBJECTIVE
  To establish a practical and effective model in predicting the premature birth, using the Back-Propagation Artificial Neural Network (BPANN).
  
  
  METHODS
  This was a prospective cohort study. Data was gathered from pregnant women selected by cluster sampling method from 2010 to 2012 in Liuyang city, Hunan province and was randomly divided into training sample (to establish the prediction models), validation sample (to select the optimal network) and testing sample (to evaluate the prediction models) by ratio of 2:1:1. BPANN and logistic regression analysis were used to establish models while ROC was applied to evaluate the 'prediction models'.
  
  
  RESULTS
  Among the 6 270 pregnant women, 265 premature births were seen, with the premature incidence as 4.22%. The 7 variables which entered into the forecasting model would include abnormal uterine or uterine deformity, parity, number of pregnancies, gestational hypertension, placenta previa, premature rupture of membrane and regular prenatal examination. Sensitivity, specificity, agreement rate and area under the ROC curve of BPANN were 67.65% , 84.87%, 84.12% and 0.795, respectively. However, the sensitivity, specificity, agreement rate and area under the ROC curve of logistic regression were 64.71%, 85.60%, 84.69% and 0.783, respectively.
  
  
  CONCLUSIONS
  The newly established BPANN model was practical and reliable, which proved that this model was slightly better than the logistic regression in the prediction of premature birth.
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Xu
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China. Email: tanhz99@qq.com.
authors.forename: Hongzhuan
authors.initials: H
authors.lastname: Tan
authors.affiliation: Liuyang Hospital for Maternal and Child Health Care.
authors.forename: Shujin
authors.initials: S
authors.lastname: Zhou
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Yue
authors.initials: Y
authors.lastname: He
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Shen
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Li
authors.initials: L
authors.lastname: Hu
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Public Health, Central South University, Changsha 410008, China.
authors.forename: Xun
authors.initials: X
authors.lastname: Li