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Prediction of Neural Tube Defect Using Support Vector Machine

机译:支持向量机在神经管缺陷预测中的应用

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摘要

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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  • 来源
    《生物医学与环境科学(英文版)》 |2010年第3期|167-172|共6页
  • 作者单位

    State key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

    State key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

    State key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

    Institute for Sustainable Water, Integrated Management & Ecosystem Research (SWIMMER), University of Liverpool, Liverpool, L69 3GP, UK;

    City University of Hong Kong, Tsinghua Graduate School at Shenzhen 518055, China;

    Institute of Population Science, Peking University, Beijing 100187, China;

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  • 入库时间 2022-08-19 04:10:09
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