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Title: Comparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes
Authors: Kazemnejad, A.
Batvandi, Z.
Faradmal, J.
Issue Date: 2010
Language: English
Abstract: 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
Description: 615-620
Keywords: Neural Networks Computer
Logistic Models
Diabetes Mellitus
Body Mass Index
Subject: Glucose Intolerance
URI: http://www.who.int/iris/handle/10665/117927
ISSN: 1020-3397
Other Identifiers: http://applications.emro.who.int/emhj/V16/06/16_6_2010_0615_0620.pdf
Appears in Collections:EMRO Journal Articles (EMHJ)



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