title: [The application of artificial neural networks to predict individual risk of essential hypertension].
abstract: OBJECTIVE
  To establish models to predict individual risk of essential hypertension and to evaluate and explore new forecasting methods.
  
  
  METHODS
  To select data of 3054 community residents from an epidemiological survey and divided them into 4:1 (2438 cases and 616 cases) ratio in accordance with the balance of age and sex to filter variables, and to establish, test and evaluate the prediction models. Using artificial neural network (ANN) and logistic regression analysis to establish models while applying ROC to evaluate the prediction models.
  
  
  RESULTS
  Forecast results of the models applying to the test set proved that ANN had lower specificity but better veracity and sensitivity than logistic regression. In particular, the Youden's index of the ANN2 came up to 0.8399 which was distinctly higher than the other two models. When the area was under the ROC curve of logistic regression, the ANN, and ANN2 models equaled to 0.732 +/- 0.026, 0.900 +/- 0.014 and 0.918 +/- 0.013 respectively, which proved that the ANN model was better in the prediction about individual health risk of essential hypertension.
  
  
  CONCLUSIONS
  Our results showed that ANN method seemed better than logistic regression in terms of predicting the individual risk from hypertension thus supplied a new method to solve the forecast of individual risk.
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
authors.forename: Shui-Hong
authors.initials: SH
authors.lastname: Zhou
authors.forename: Shao-Fa
authors.initials: SF
authors.lastname: Nie
authors.forename: Chong-Jian
authors.initials: CJ
authors.lastname: Wang
authors.forename: Sheng
authors.initials: S
authors.lastname: Wei
authors.forename: Yi-Hua
authors.initials: YH
authors.lastname: Xu
authors.forename: Xue-Hua
authors.initials: XH
authors.lastname: Li
authors.forename: En-Min
authors.initials: EM
authors.lastname: Song
mesh_terms: D006801:Humans; D006973:Hypertension / Q000453:epidemiology*; D016015:Logistic Models; D016571:Neural Networks, Computer; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D012984:Software