期刊
EXPERIMENTAL AND CLINICAL ENDOCRINOLOGY & DIABETES
卷 124, 期 1, 页码 34-38出版社
JOHANN AMBROSIUS BARTH VERLAG MEDIZINVERLAGE HEIDELBERG GMBH
DOI: 10.1055/s-0035-1565175
关键词
glucose; overweight; regression; BP-ANN
资金
- key construction academic subject (medical innovation) of Zhejiang Province [11-CX26]
- National Natural Science Foundation of China [81100993, 81171787, 81300311, 81360212, 81571297, 81500519, 81541036, 81560229]
- Opening Project of Zhejiang Provincial Top Key Discipline of Pharmaceutical Sciences, Zhejiang Provincial Natural Science Foundation [LY12H03001, LY12H15001, LQ13H280002]
- Project of Medical Technology of Zhejiang province [2015KYB234]
- Public Project of Science and Technology of Wenzhou City [Y20140739]
- Wenling Foundation of Science and Technology [2011WLCB0109, 2014C311051]
- Ningbo Natural Science Foundation [2015A610234]
- Xiangshan Science and Technology Project [2015C6005]
- Wenzhou Medical University [QTJ15001]
Background: Overweight individuals are at higher risk for developing type II diabetes than the general population. We conducted this study to analyze the correlation between blood glucose and biochemical parameters, and developed a blood glucose prediction model tailored to overweight patients. Methods: A total of 346 overweight Chinese people patients ages 18-81 years were involved in this study. Their levels of fasting glucose (fs-GLU), blood lipids, and hepatic and renal functions were measured and analyzed by multiple linear regression (MLR). Based the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tansig as the transfer function of the hidden layers nodes, and purelin for the output layer nodes, with training goal of 0.5x10(-5). Results: There was significant correlation between fs-GLU with age, BMI, and blood biochemical indexes (P<0.05). The results of MLR analysis indicated that age, fasting alanine transaminase (fs-ALT), blood urea nitrogen (fs-BUN), total protein (fs-TP), uric acid (fs-BUN), and BMI are 6 independent variables related to fs-GLU. Based on these parameters, the BP-ANN model was performed well and reached high prediction accuracy when training 1000 epoch (R=0.9987). Conclusions: The level of fs-GLU was predictable using the proposed BP-ANN model based on 6 related parameters (age, fs-ALT, fs-BUN, fs-TP, fs-UA and BMI) in overweight patients.
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