Predicting Type 2 Diabetes Using an Electronic Nose-based Artificial Neural Network Analysis

TitelPredicting Type 2 Diabetes Using an Electronic Nose-based Artificial Neural Network Analysis
Publication TypeJournal Article
Year of Publication2002
AuthorsMohamed E.I., Linder R., Perriello G., Di Daniele N., Pöppl S.J., De Lorenzo A.
JournalDiabetes, nutrition & metabolism
Volume15
Issue4
Pages215-21
Date Published2002 Aug
Publication Languageeng
ISSN0394-3402
SchlüsselwörterAged, 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
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.

PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/12416658?dopt=Abstract

Alternate JournalDiabetes Nutr. Metab.
Erstellt am 9. November 2012 - 16:09 von Kulbe.

Studium

Medizinische Informatik
an der Uni Lübeck studieren

Informationen für
Interessierte
u. Einsteiger

Anschrift

Institutssekretariat
Susanne Petersen

Tel+49 451 3101 5601
Fax+49 451 3101 5604


Gebäude 64 (Informatik)

Ratzeburger Allee 160
23538 Lübeck
Deutschland