Predicting Type 2 Diabetes Using an Electronic Nose-based Artificial Neural Network Analysis - Mohamed, E I; Linder, Roland; Perriello, G; Di Daniele, N; Siegfried J. Pöppl; De Lorenzo, A (Diabetes, nutrition & metabolism, 2002)
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 "Approximation and Classification of Medical Data" (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.
Keywords: Aged; Blood Glucose; Body Mass Index; Breath Tests; Diabetes Mellitus, Type 2; Fasting; Female; Glycosuria; Humans; Logistic Models; Male; Middle Aged; Neural Networks (Computer); Nose; Odors; Proteinuria; Sensitivity and Specificity