title: Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population.
abstract: BACKGROUND
  The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population.
  
  
  METHODS
  We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30-80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set.
  
  
  RESULTS
  Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732-0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0.
  
  
  CONCLUSIONS
  ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.
authors.forename: Juanmei
authors.initials: J
authors.lastname: Liu
authors.forename: Zi-Hui
authors.initials: ZH
authors.lastname: Tang
authors.forename: Fangfang
authors.initials: F
authors.lastname: Zeng
authors.forename: Zhongtao
authors.initials: Z
authors.lastname: Li
authors.forename: Linuo
authors.initials: L
authors.lastname: Zhou
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000175:diagnosis*; D002681:China; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011379:Prognosis; D012307:Risk Factors
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