title: A risk assessment model for type 2 diabetes in Chinese.
abstract: OBJECTIVE
  To develop a risk assessment model for persons at risk from type 2 diabetes in Chinese.
  
  
  METHODS
  The model was generated from the cross-sectional data of 16246 persons aged from 20 years old and over. C4.5 algorithm and multivariate logistic regression were used for variable selection. Relative risk value combined with expert decision constructed a comprehensive risk assessment for evaluating the individual risk category. The validity of the model was tested by cross validation and a survey performed six years later with some participants.
  
  
  RESULTS
  Nine variables were selected as risk variables. A mathematical model was established to calculate the average probability of diabetes in each cluster's group divided by sex and age. A series of criteria combined with relative RR value (2.2) and level of risk variables stratified individuals into four risk groups (non, low, medium and high risk). The overall accuracy reached 90.99% evaluated by cross-validation inside the model population. The incidence of diabetes for each risk group increased from 1.5 (non-risk group) to 28.2(high-risk group) per one thousand persons per year with six years follow-up.
  
  
  CONCLUSIONS
  The model could determine the individual risk for type 2 diabetes by four risk degrees. This model could be used as a technique tool not only to support screening persons at different risk, but also to evaluate the result of the intervention.
authors.affiliation: Information System and Security & Countermeasures Experimental Center, Beijing Institute of Technology, Beijing, P. R. China.
authors.forename: Senlin
authors.initials: S
authors.lastname: Luo
authors.affiliation: Information System and Security & Countermeasures Experimental Center, Beijing Institute of Technology, Beijing, P. R. China.
authors.forename: Longfei
authors.initials: L
authors.lastname: Han
authors.affiliation: Beijing Institute of Geriatrics, Beijing Hospital, Ministry of Health, Beijing, P. R. China.
authors.forename: Ping
authors.initials: P
authors.lastname: Zeng
authors.affiliation: College of Public Health, Nanjing Medical University, Nanjing, Jiangsu, P. R. China.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
authors.affiliation: Information System and Security & Countermeasures Experimental Center, Beijing Institute of Technology, Beijing, P. R. China.
authors.forename: Limin
authors.initials: L
authors.lastname: Pan
authors.affiliation: Beijing Institute of Geriatrics, Beijing Hospital, Ministry of Health, Beijing, P. R. China.
authors.forename: Shu
authors.initials: S
authors.lastname: Wang
authors.affiliation: Beijing Institute of Geriatrics, Beijing Hospital, Ministry of Health, Beijing, P. R. China.
authors.forename: Tiemei
authors.initials: T
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000368:Aged; D044466:Asian People; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D003430:Cross-Sectional Studies; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D006801:Humans; D015994:Incidence; D008075:Lipoproteins, HDL / Q000097:blood; D008077:Lipoproteins, LDL / Q000097:blood; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D018570:Risk Assessment; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood; D055105:Waist Circumference
chemical_list: D008075:Lipoproteins, HDL; D008077:Lipoproteins, LDL; D014280:Triglycerides; D002784:Cholesterol
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