The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes.

Journal: Medical decision making : an international journal of the Society for Medical Decision Making
Published Date: Jan 1, 2016

Abstract

OBJECTIVE: To evaluate the impact of the synthetic minority oversampling technique (SMOTE) on the performance of probabilistic neural network (PNN), naïve Bayes (NB), and decision tree (DT) classifiers for predicting diabetes in a prospective cohort of the Tehran Lipid and Glucose Study (TLGS).

Authors

  • Azra Ramezankhani
    Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (AR, FH, DK)
  • Omid Pournik
    Department of Community Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran (OP)
  • Jamal Shahrabi
    Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran (JS)
  • Fereidoun Azizi
    Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (FA)
  • Farzad Hadaegh
    Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (AR, FH, DK)
  • Davood Khalili
    Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (AR, FH, DK)

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