title: The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes.
abstract: 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).
  
  
  
  . Data of the 6647 nondiabetic participants, aged 20 years or older with more than 10 years of follow-up, were used to develop prediction models based on 21 common risk factors. The minority class in the training dataset was oversampled using the SMOTE technique, at 100%, 200%, 300%, 400%, 500%, 600%, and 700% of its original size. The original and the oversampled training datasets were used to establish the classification models. Accuracy, sensitivity, specificity, precision, F-measure, and Youden's index were used to evaluated the performance of classifiers in the test dataset. To compare the performance of the 3 classification models, we used the ROC convex hull (ROCCH).
  
  
  
  Oversampling the minority class at 700% (completely balanced) increased the sensitivity of the PNN, DT, and NB by 64%, 51%, and 5%, respectively, but decreased the accuracy and specificity of the 3 classification methods. NB had the best Youden's index before and after oversampling. The ROCCH showed that PNN is suboptimal for any class and cost conditions.
  
  
  
  To determine a classifier with a machine learning algorithm like the PNN and DT, class skew in data should be considered. The NB and DT were optimal classifiers in a prediction task in an imbalanced medical database.
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (AR, FH, DK)
authors.forename: Azra
authors.initials: A
authors.lastname: Ramezankhani
authors.affiliation: Department of Community Medicine, School of Medicine, Iran University of Medical Sciences, Tehran, Iran (OP)
authors.forename: Omid
authors.initials: O
authors.lastname: Pournik
authors.affiliation: Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran (JS)
authors.forename: Jamal
authors.initials: J
authors.lastname: Shahrabi
authors.affiliation: Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (FA)
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, Tehran, Iran (AR, FH, DK)
authors.forename: Farzad
authors.initials: F
authors.lastname: Hadaegh
authors.affiliation: Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran (AR, FH, DK)|Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran (DK).
authors.forename: Davood
authors.initials: D
authors.lastname: Khalili
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D003663:Decision Trees; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D006801:Humans; D007492:Iran; D008297:Male; D008875:Middle Aged; D008913:Minority Groups; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012307:Risk Factors; D015983:Selection Bias; D012680:Sensitivity and Specificity; D012959:Socioeconomic Factors
keywords: SMOTE; classification; data mining; diabetes