title: Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network.
abstract: BACKGROUND
  We aimed to assess the high-risk group for suicide using different classification methods includinglogistic regression (LR), decision tree (DT), artificial neural network (ANN), and support vector machine (SVM).
  
  
  METHODS
  We used the dataset of a study conducted to predict risk factors of completed suicide in Hamadan Province, the west of Iran, in 2010. To evaluate the high-risk groups for suicide, LR, SVM, DT and ANN were performed. The applied methods were compared using sensitivity, specificity, positive predicted value, negative predicted value, accuracy and the area under curve. Cochran-Q test was implied to check differences in proportion among methods. To assess the association between the observed and predicted values, Ø coefficient, contingency coefficient, and Kendall tau-b were calculated.
  
  
  RESULTS
  Gender, age, and job were the most important risk factors for fatal suicide attempts in common for four methods. SVM method showed the highest accuracy 0.68 and 0.67 for training and testing sample, respectively. However, this method resulted in the highest specificity (0.67 for training and 0.68 for testing sample) and the highest sensitivity for training sample (0.85), but the lowest sensitivity for the testing sample (0.53). Cochran-Q test resulted in differences between proportions in different methods (P<0.001). The association of SVM predictions and observed values, Ø coefficient, contingency coefficient, and Kendall tau-b were 0.239, 0.232 and 0.239, respectively.
  
  
  CONCLUSIONS
  SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN.
authors.affiliation: Dept. of Epidemiology & Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.
authors.forename: Payam
authors.initials: P
authors.lastname: Amini
authors.affiliation: Dept. of Biostatistics & Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Hasan
authors.initials: H
authors.lastname: Ahmadinia
authors.affiliation: Research Center for Health Sciences and Dept. of Biostatistics & Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Jalal
authors.initials: J
authors.lastname: Poorolajal
authors.affiliation: Dept. of Biostatistics & Epidemiology, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Moqaddasi Amiri
keywords: Classification; Decision tree; Logistic regression; Neuralnetworks; Suicide; Support vector machine