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Prediction of neural tube defect using support vector machine
Jin-Feng Wang1, Xin Liu, Yi-Lan Liao
1State 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
Objective:
To predict neural tube birth defect (NTD) using support vector machine (SVM).
Method:
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.
Result:
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.
Conclusion:
Results from this study have shown that SVM is applicable to the prediction of NTD.