title: A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results.
abstract: OBJECTIVE
  Identifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study was to show the application of some variable selection methods, usually used in data mining, for an epidemiological study. We introduce here a systematic approach.
  
  
  METHODS
  The P-value-based method, usually used in epidemiological studies, and several filter and wrapper methods were implemented to select the predictors of diabetes among 55 variables in 803 prediabetic females, aged ≥ 20 years, followed for 10-12 years. To develop a logistic model, variables were selected from a train data set and evaluated on the test data set. The measures of Akaike information criterion (AIC) and area under the curve (AUC) were used as performance criteria. We also implemented a full model with all 55 variables.
  
  
  RESULTS
  We found that the worst and the best models were the full model and models based on the wrappers, respectively. Among filter methods, symmetrical uncertainty gave both the best AUC and AIC.
  
  
  CONCLUSIONS
  Our experiment showed that the variable selection methods used in data mining could improve the performance of clinical prediction models. An R program was developed to make these methods more feasible and visualize the results.
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, 1985717413 Tehran, Iran.
authors.forename: Farideh
authors.initials: F
authors.lastname: Bagherzadeh-Khiabani
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, 1985717413 Tehran, Iran.
authors.forename: Azra
authors.initials: A
authors.lastname: Ramezankhani
authors.affiliation: Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, 1985717413 Tehran, Iran.
authors.forename: Fereidoun
authors.initials: F
authors.lastname: Azizi
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, 1985717413 Tehran, Iran.
authors.forename: Farzad
authors.initials: F
authors.lastname: Hadaegh
authors.affiliation: Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, 1985717413 Tehran, Iran; Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Velenjak, 1985717413 Tehran, Iran. Electronic address: dkhalili@endocrine.ac.ir.
authors.forename: Davood
authors.initials: D
authors.lastname: Khalili
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001185:Artificial Intelligence; D057225:Data Mining / Q000706:statistics & numerical data*; D003920:Diabetes Mellitus / Q000453:epidemiology*; D016021:Epidemiologic Studies; D005260:Female; D006801:Humans; D007492:Iran / Q000453:epidemiology; D016015:Logistic Models; D008962:Models, Theoretical
keywords: Data mining; Feature selection; Methods; Prediction; Statistical model; Variable selection