title: Evaluating the risk of type 2 diabetes mellitus using artificial neural network: an effective classification approach.
abstract: OBJECTIVE
  To develop and evaluate an effective classification approach without biochemical parameters to identify those at high risk of T2DM in rural adults.
  
  
  METHODS
  A cross-sectional survey was conducted. Of 8640 subjects who met inclusion criteria, 75% (N1=6480) were randomly selected to provide training set for constructing artificial neural network (ANN) and multivariate logistic regression (MLR) models. The remaining 25% (N2=2160) were assigned to validation set for performance comparisons of the ANN and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the validation set.
  
  
  RESULTS
  The prevalence rates of T2DM were 8.66% (n=561) and 9.21% (n=199) in training and validation sets, respectively. For ANN model, the sensitivity, specificity, positive and negative predictive value for identifying T2DM were 86.93%, 79.14%, 31.86%, and 98.18%, respectively, while MLR model were only 60.80%, 75.48%, 21.78%, and 94.52%, respectively. Area under the ROC curve (AUC) value for identifying T2DM when using the ANN model was 0.891, showing more accurate predictive performance than the MLR model (AUC=0.744) (P=0.0001).
  
  
  CONCLUSIONS
  The ANN model is an effective classification approach for identifying those at high risk of T2DM based on demographic, lifestyle and anthropometric data.
authors: Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
authors: Chongjian
authors: C
authors: Wang
authors: Linlin
authors: L
authors: Li
authors: Ling
authors: L
authors: Wang
authors: Zhiguang
authors: Z
authors: Ping
authors: Muanda Tsobo
authors: MT
authors: Flory
authors: Gaoshuai
authors: G
authors: Wang
authors: Yuanlin
authors: Y
authors: Xi
authors: Wenjie
authors: W
authors: Li
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology* / Q000517:prevention & control; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015995:Prevalence; D012372:ROC Curve; D018570:Risk Assessment; D012424:Rural Population / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity