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...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier Ltd
2010
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/43499 |
| _version_ | 1848756711290044416 |
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| author | Wang, J. Liu, Xin Liao, Y. Chen, H. Li, W. Zheng, X. |
| author_facet | Wang, J. Liu, Xin Liao, Y. Chen, H. Li, W. Zheng, X. |
| author_sort | Wang, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T09:16:32Z |
| format | Journal Article |
| id | curtin-20.500.11937-43499 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:16:32Z |
| publishDate | 2010 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-434992018-03-29T09:07:08Z Prediction of Neural Tube Defect Using Support Vector Machine Wang, J. Liu, Xin Liao, Y. Chen, H. Li, W. Zheng, X. Prediction NTD Small sample SVM 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 2010 Journal Article http://hdl.handle.net/20.500.11937/43499 10.1016/S0895-3988(10)60048-7 Elsevier Ltd restricted |
| spellingShingle | Prediction NTD Small sample SVM Wang, J. Liu, Xin Liao, Y. Chen, H. Li, W. Zheng, X. Prediction of Neural Tube Defect Using Support Vector Machine |
| title | Prediction of Neural Tube Defect Using Support Vector Machine |
| title_full | Prediction of Neural Tube Defect Using Support Vector Machine |
| title_fullStr | Prediction of Neural Tube Defect Using Support Vector Machine |
| title_full_unstemmed | Prediction of Neural Tube Defect Using Support Vector Machine |
| title_short | Prediction of Neural Tube Defect Using Support Vector Machine |
| title_sort | prediction of neural tube defect using support vector machine |
| topic | Prediction NTD Small sample SVM |
| url | http://hdl.handle.net/20.500.11937/43499 |