title: Multiple Linear Regression and Artificial Neural Network to Predict Blood Glucose in Overweight Patients.
abstract: 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 0.5×10(-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 1 000 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.
authors.affiliation: Department of Infection and Liver Diseases, Liver Research Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
authors.forename: J
authors.initials: J
authors.lastname: Wang
authors.affiliation: Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China.
authors.forename: F
authors.initials: F
authors.lastname: Wang
authors.affiliation: Wenzhou Medical University, Wenzhou, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Liu
authors.affiliation: The Affiliated Wenling Hospital of Wenzhou Medial University, Wenling, China.
authors.forename: J
authors.initials: J
authors.lastname: Xu
authors.affiliation: Wenzhou Medical University, Wenzhou, China.
authors.forename: H
authors.initials: H
authors.lastname: Lin
authors.affiliation: Inner Mongolia Medical University, Hohhot, China.
authors.forename: B
authors.initials: B
authors.lastname: Jia
authors.affiliation: Ningbo Fourth Hospital, Xiangshan, China.
authors.forename: W
authors.initials: W
authors.lastname: Zuo
authors.affiliation: Ningbo Fourth Hospital, Xiangshan, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Department of Pharmacy, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
authors.forename: L
authors.initials: L
authors.lastname: Hu
authors.affiliation: Laboratory of Internal Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
authors.forename: F
authors.initials: F
authors.lastname: Lin
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001786:Blood Glucose / Q000378:metabolism*; D005215:Fasting / Q000097:blood; D005260:Female; D006801:Humans; D016014:Linear Models; D008055:Lipids / Q000097:blood; D008099:Liver / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D050177:Overweight / Q000097:blood*; D011237:Predictive Value of Tests
chemical_list: D001786:Blood Glucose; D008055:Lipids