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Prediction of Neural Tube Defect Using Support Vector Machine

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Authors
Wang, J.
Liu, Xin
Liao, Y.
Chen, H.
Li, W.
Zheng, X.
Date
2010
Type
Journal Article

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Wang, J. and Liu, X. and Liao, Y. and Chen, H. and Li, W. and Zheng, X. 2010. Prediction of Neural Tube Defect Using Support Vector Machine. Biomedical and Environmental Sciences. 23: pp. 167-172.
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Biomedical and Environmental Sciences
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Abstract

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