<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed, E I</style></author><author><style face="normal" font="default" size="100%">Linder, Roland</style></author><author><style face="normal" font="default" size="100%">Perriello, G</style></author><author><style face="normal" font="default" size="100%">Di Daniele, N</style></author><author><style face="normal" font="default" size="100%">Siegfried J. Pöppl</style></author><author><style face="normal" font="default" size="100%">De Lorenzo, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Predicting Type 2 Diabetes Using an Electronic Nose-based Artificial Neural Network Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Diabetes, nutrition &amp; metabolism</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Diabetes Nutr. Metab.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Blood Glucose</style></keyword><keyword><style  face="normal" font="default" size="100%">Body Mass Index</style></keyword><keyword><style  face="normal" font="default" size="100%">Breath Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Diabetes Mellitus, Type 2</style></keyword><keyword><style  face="normal" font="default" size="100%">Fasting</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Glycosuria</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Logistic Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks (Computer)</style></keyword><keyword><style  face="normal" font="default" size="100%">Nose</style></keyword><keyword><style  face="normal" font="default" size="100%">Odors</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteinuria</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2002 Aug</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">215-21</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Diabetes is a major health problem in both industrial and developing countries, and its incidence is rising. Although detection of diabetes is improving, about half of the patients with Type 2 diabetes are undiagnosed and the delay from disease onset to diagnosis may exceed 10 yr. Thus, earlier detection of Type 2 diabetes and treatment of hyperglycaemia and related metabolic abnormalities is of vital importance. The objectives of the present study were to examine urine samples from Type 2 diabetic patients and healthy volunteers using the electronic nose technology and to evaluate possible application of data classification methods such as self-learning artificial neural networks (ANN) and logistic regression (LR) in comparison with principal components analysis (PCA). Urine samples from Type 2 diabetic patients and healthy controls were processed randomly using a simple 8-sensors electronic nose and individual electronic nose patterns were qualitatively classified using the &quot;Approximation and Classification of Medical Data&quot; (ACMD) network based on 2 output neurons, binary LR analysis and PCA. Distinct classes were found for Type 2 diabetic subjects and controls using PCA, which had a 96.0% successful classification percentage mean while qualitative ANN analysis and LR analysis had successful classification percentages of 92.0% and 88.0%, respectively. Therefore, the ACMD network is suitable for classifying medical and clinical data.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/12416658?dopt=Abstract</style></custom1></record></records></xml>