Mohamed E I, Linder R, Perriello G, Di Daniele N, Pöppl S J, De Lorenzo A
Human Physiology Division, University Tor Vergata, Rome, Italy.
Diabetes Nutr Metab. 2002 Aug;15(4):215-21.
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.
糖尿病在工业化国家和发展中国家都是一个主要的健康问题，并且其发病率正在上升。尽管糖尿病的检测正在改善，但约一半的2型糖尿病患者未被诊断出来，从疾病发作到诊断的延迟可能超过10年。因此，早期检测2型糖尿病并治疗高血糖及相关代谢异常至关重要。本研究的目的是使用电子鼻技术检测2型糖尿病患者和健康志愿者的尿液样本，并评估数据分类方法（如自学习人工神经网络（ANN）和逻辑回归（LR））与主成分分析（PCA）相比的可能应用。使用一个简单的8传感器电子鼻对2型糖尿病患者和健康对照的尿液样本进行随机处理，并基于2个输出神经元、二元LR分析和PCA，使用“医学数据的近似与分类”（ACMD）网络对个体电子鼻模式进行定性分类。使用PCA发现2型糖尿病受试者和对照组有明显的类别，其平均成功分类百分比为96.0%，而定性ANN分析和LR分析的成功分类百分比分别为92.0%和88.0%。因此，ACMD网络适用于对医学和临床数据进行分类。
