title: Prediction of neural tube defect using support vector machine.
abstract: OBJECTIVE
  To predict neural tube birth defect (NTD) using support vector machine (SVM).
  
  
  METHODS
  The dataset in the pilot area was divided into non overlaid training set and testing set. SVM was trained using the training set and the trained SVM was then used to predict the classification of NTD.
  
  
  RESULTS
  NTD rate was predicted at village level in the pilot area. The accuracy of the prediction was 71.50% for the training dataset and 68.57% for the test dataset respectively.
  
  
  CONCLUSIONS
  Results from this study have shown that SVM is applicable to the prediction of NTD.
authors: State key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. wangjf@Lreis.ac.cn
authors: Jin-Feng
authors: JF
authors: Wang
authors: Xin
authors: X
authors: Liu
authors: Yi-Lan
authors: YL
authors: Liao
authors: Hong-Yan
authors: HY
authors: Chen
authors: Wan-Xin
authors: WX
authors: Li
authors: Xiao-Ying
authors: XY
authors: Zheng
mesh_terms: D002681:China / Q000453:epidemiology; D006801:Humans; D009436:Neural Tube Defects / Q000453:epidemiology*; D010865:Pilot Projects