Prediction of neural tube defect using support vector machine.
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.
Wang
Jin-Feng
JF
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
Liu
Xin
X
Liao
Yi-Lan
YL
Chen
Hong-Yan
HY
Li
Wan-Xin
WX
Zheng
Xiao-Ying
XY
D002681:China / Q000453:epidemiology; D006801:Humans; D009436:Neural Tube Defects / Q000453:epidemiology*; D010865:Pilot Projects