title: Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis.
abstract: 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.
authors.affiliation: Human Physiology Division, University Tor Vergata, Rome, Italy.
authors.forename: E I
authors.initials: EI
authors.lastname: Mohamed
authors.forename: R
authors.initials: R
authors.lastname: Linder
authors.forename: G
authors.initials: G
authors.lastname: Perriello
authors.forename: N
authors.initials: N
authors.lastname: Di Daniele
authors.forename: S J
authors.initials: SJ
authors.lastname: Pöppl
authors.forename: A
authors.initials: A
authors.lastname: De Lorenzo
mesh_terms: D000368:Aged; D001786:Blood Glucose / Q000032:analysis; D015992:Body Mass Index; D001944:Breath Tests; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000652:urine*; D005215:Fasting; D005260:Female; D006029:Glycosuria; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009666:Nose; D009812:Odorants / Q000032:analysis*; D011507:Proteinuria / Q000652:urine; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose