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钱玲, 施侣元, 程茂金. 应用人工神经网络预测糖尿病/糖耐量异常[J]. 中国公共卫生, 2003, 19(10): 1272-1274.
引用本文: 钱玲, 施侣元, 程茂金. 应用人工神经网络预测糖尿病/糖耐量异常[J]. 中国公共卫生, 2003, 19(10): 1272-1274.
QIAN Ling, SHI L, CHENG Mao-jin. Application of ANN on prediction of abnormal DM/IGT[J]. Chinese Journal of Public Health, 2003, 19(10): 1272-1274.
Citation: QIAN Ling, SHI L, CHENG Mao-jin. Application of ANN on prediction of abnormal DM/IGT[J]. Chinese Journal of Public Health, 2003, 19(10): 1272-1274.

应用人工神经网络预测糖尿病/糖耐量异常

Application of ANN on prediction of abnormal DM/IGT

  • 摘要:
      目的   在流行病学调查资料的基础上, 探讨学习向量量化(LVQ)网络用于糖尿病(DM)/糖耐量异常(IGT)疾病状态的分类预测的前景。
      方法   采用LVQ网络和判别分析方法对某矿区糖尿病现况调查资料和某综合性医院的DM病例-对照资料, 进行DM/IGT/正常状态的判别比较; 同时人为设置变量缺损值, 检验LVQ网络对缺失数据的适应性。
      结果   LVQ网络结构为25→13→3;网络判断DM、IGT的灵敏度分别为7045%、6479%, 特异度为100.00%, 准确度为96.98%, 对血糖异常者的正确判断率为92.45%。利用逐步判别分析建立的含11个变量的判别方程判断DM、IGT的灵敏度分别为67.05%、60.56%, 特异度为89.75%, 准确率为87.34%, 对血糖异常者的正确判断率为85.53%.对来自某综合性医院的DM病例-对照资料进行模型验证发现, LVQ网络预测效果优于判别的分析, 网络能识别出全部对照及92.37%的病例, 判别准确率为96.19%。LVQ网络对带缺失项样本的误判比例为1/30, 判别分析则为7/30。
      结论   利用LVQ网络进行疾病分类预测, 不仅能获得更好的预测效果, 而且对资料的类型、分布不作任何限制, 也不需要对分析变量做任何处理, 还能很好地处理带缺失项的资料, 是一种很好的流行病学分类预测新方法。

     

    Abstract:
      Objective   To discuss the application potential of ANN on epidemologic classification of disease.
      Methods   LVQNN and discriminant analysis were applied in terms of the data of epidemiological survey and the case-control study in a general hospital.
      Results   The structure of LVQNN was 25→13→3, the sensitivity of model prediction to DM and IGT was 70.45%, 64.79% respectively, the specificity was 100.00%, the coincident rate was 96.98%, and the veracity to abnormal blood glucose individuals was 92.45%.Through stepwise discriminant analysis, the discriminant equations were established including 11 variables, the sensitivity of equaions to DM and IGT was 67.05%, 60.56% respectively, the specificity was 89.75%, the coincident rate was 87.34%, and the veracity to abnormal blood glucose individuals was 85.53%.To the data from a general hospital, all the collators and 92.37% of DM patients were recognized by LVQ with a veracity of 96.19%, excelling to discriminant analysis in predicting the status of disease. Further analysis on 30 cases with missing values showed that the disagreement ratio of LVQ was 1/30, lower than that of discriminant analysis of 7/30.
      Conclusion   Compared with the conventional statistic method, LVQ not only had better prediction precision, but also can treat the data with missing values satisfactorily, and had no limit to the type and distribution of data. It would be a new powerful method to epidemiologic prediction.

     

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