title: Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.
abstract: OBJECTIVE
  The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database.
  
  
  METHODS
  For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures.
  
  
  RESULTS
  We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status.
  
  
  CONCLUSIONS
  In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes.
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Azra
authors.initials: A
authors.lastname: Ramezankhani
authors.affiliation: Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Informatics Research Center, Faculty of Medicine, Mashhad, Iran.
authors.forename: Omid
authors.initials: O
authors.lastname: Pournik
authors.affiliation: Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran.
authors.forename: Jamal
authors.initials: J
authors.lastname: Shahrabi
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Davood
authors.initials: D
authors.lastname: Khalili
authors.affiliation: Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
authors.forename: Fereidoun
authors.initials: F
authors.lastname: Azizi
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: fzhadaegh@endocrine.ac.ir.
authors.forename: Farzad
authors.initials: F
authors.lastname: Hadaegh
mesh_terms: D000328:Adult; D000368:Aged; D062186:Arterial Pressure; D001786:Blood Glucose / Q000032:analysis; D015992:Body Mass Index; D001837:Body Weights and Measures; D019295:Computational Biology; D057225:Data Mining; D003661:Decision Support Techniques; D003663:Decision Trees; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology*; D004522:Educational Status; D004651:Employment; D005260:Female; D006801:Humans; D015994:Incidence; D007492:Iran / Q000453:epidemiology; D008137:Longitudinal Studies; D008297:Male; D017533:Marital Status; D008875:Middle Aged; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012907:Smoking; D014280:Triglycerides / Q000097:blood
chemical_list: D001786:Blood Glucose; D014280:Triglycerides
keywords: Decision tree; Prediction model; Type 2 diabetes