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.

Mohamed, E., Linder, R., Perriello, G., DI DANIELE, N., Pöppl, S., DE LORENZO, A. (2002). Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. DIABETES, NUTRITION & METABOLISM, 15(4), 215-21.

Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis

DI DANIELE, NICOLA;DE LORENZO, ANTONINO
2002-08-01

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.
ago-2002
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore MED/49 - SCIENZE TECNICHE DIETETICHE APPLICATE
English
Sensitivity and Specificity; Blood Glucose; Male; Middle Aged; Odors; Female; Neural Networks (Computer); Logistic Models; Proteinuria; Glycosuria; Diabetes Mellitus, Type 2; Humans; Body Mass Index; Fasting; Breath Tests; Aged; Nose
Mohamed, E., Linder, R., Perriello, G., DI DANIELE, N., Pöppl, S., DE LORENZO, A. (2002). Predicting Type 2 diabetes using an electronic nose-based artificial neural network analysis. DIABETES, NUTRITION & METABOLISM, 15(4), 215-21.
Mohamed, E; Linder, R; Perriello, G; DI DANIELE, N; Pöppl, S; DE LORENZO, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/12205
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