人工神经网络应用于糖尿病/糖耐量异常的疾病分类研究

目的 探讨人工神经网络(ANN)用于疾病分类研究的前景。方法 利用某矿区1996年糖尿病现况调查资料，采用学习向量量化(LVQ)网络和判别分析方法进行糖尿病借耐量(DM／IGT)异常／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；网络判断准确率为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个变量的判别方程的判断准确率为87．34％，对血糖异常者的正确判断率为85．53％。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。

Study on the application of artificial neural network on diabetes mellitus/insulin-glucose tolerance classification

OBJECTIVE: To discuss the potential application of artificial neural network (ANN) on the epidemiological classification of disease. METHODS: Learning vector quantification neural network (LVQNN) and discriminate analysis were applied to data from epidemiological survey in a mine in 1996. RESULTS: The structure of LVQNN was 25-->13-->3. The total veracity rates was 96.98%, and 92.45% among the abnormal blood glucose individuals. Through stepwise discriminate analysis, the discriminate equations were established including 11 variables with a total veracity rate of 87.34%, but was 85.53% in the abnormal blood glucose individuals. Further analysis on 30 cases with missing values showed that the disagreement ratio of LVQ was 1/30, lower than that of discriminate analysis of 7/30. CONCLUSIONS: Compared to the conventional statistics method, LVQ not only showed better prediction precision, but could treat data with missing values satisfactorily plus it had no limit to the type or distribution of relevant data, thus provided a new powerful method to epidemiologic prediction.