Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Islamic Republic of Iran.
East Mediterr Health J. 2010 Jun;16(6):615-20.
Models based on an artificial neural network (the multilayer perceptron) and binary logistic regression were compared in their ability to differentiate between disease-free subjects and those with impaired glucose tolerance or diabetes mellitus diagnosed by fasting plasma glucose. Demographic, anthropometric and clinical data were collected from 7222 participants aged 30-88 years in the Tehran Lipid and Glucose Study. The kappa statistics were 0.229 and 0.218 and the area under the ROC curves were 0.760 and 0.770 for the logistic regression and perceptron respectively. There was no performance difference between models based on logistic regression and an artificial neural network for differentiating impaired glucose tolerance/diabetes patients from disease-free patients.
基于人工神经网络（多层感知器）和二项逻辑回归的模型在区分无病受试者与通过空腹血浆葡萄糖诊断为糖耐量受损或糖尿病患者的能力方面进行了比较。德黑兰脂质和血糖研究共纳入 7222 名年龄在 30-88 岁的参与者，收集了他们的人口统计学、人体测量学和临床数据。逻辑回归和感知器的kappa 统计量分别为 0.229 和 0.218，ROC 曲线下面积分别为 0.760 和 0.770。基于逻辑回归和人工神经网络的模型在区分糖耐量受损/糖尿病患者与无病患者方面没有性能差异。
