Prediction of Neural Tube Defect Using Support Vector Machine

Prediction of Neural Tube Defect Using Support Vector Machine

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. Resu...

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Bibliographic Details
Main Authors: Wang, J., Liu, Xin, Liao, Y., Chen, H., Li, W., Zheng, X.
Format: Journal Article
Published: Elsevier Ltd 2010
Subjects:
Prediction
NTD
Small sample
SVM
Online Access:http://hdl.handle.net/20.500.11937/43499
Description
Summary: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

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