title: Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.
abstract: BACKGROUND
  Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.
  
  
  METHODS
  Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35-78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.
  
  
  RESULTS
  Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).
  
  
  CONCLUSIONS
  The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.
authors.affiliation: Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
authors.forename: Chong-Jian
authors.initials: CJ
authors.lastname: Wang
authors.forename: Yu-Qian
authors.initials: YQ
authors.lastname: Li
authors.forename: Ling
authors.initials: L
authors.lastname: Wang
authors.forename: Lin-Lin
authors.initials: LL
authors.lastname: Li
authors.forename: Yi-Rui
authors.initials: YR
authors.lastname: Guo
authors.forename: Ling-Yun
authors.initials: LY
authors.lastname: Zhang
authors.forename: Mei-Xi
authors.initials: MX
authors.lastname: Zhang
authors.forename: Rong-Hai
authors.initials: RH
authors.lastname: Bie
mesh_terms: D000328:Adult; D000368:Aged; D002681:China / Q000453:epidemiology; D050171:Dyslipidemias / Q000453:epidemiology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D018570:Risk Assessment / Q000379:methods*; D012424:Rural Population / Q000706:statistics & numerical data*