Comparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes

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dc.creatorKazemnejad, A.
dc.creatorBatvandi, Z.
dc.creatorFaradmal, J.
dc.date2014-06-17T09:12:29Z
dc.date2014-06-17T09:12:29Z
dc.date2010-12-31
dc.date.accessioned2026-08-03T04:27:49Z
dc.description615-620
dc.descriptionModels 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
dc.formatapplication/pdf
dc.identifier1020-3397
dc.identifierhttp://applications.emro.who.int/emhj/V16/06/16_6_2010_0615_0620.pdf
dc.identifierhttps://iris.who.int/handle/10665/117927
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/189648
dc.languageEnglish
dc.languageen
dc.relationEMHJ - Eastern Mediterranean Health Journal, 16 (6), 615-620, 2010
dc.subjectNeural Networks Computer
dc.subjectLogistic Models
dc.subjectDiabetes Mellitus
dc.subjectBody Mass Index
dc.subjectGlucose Intolerance
dc.titleComparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes

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