suicide is an important health challengeand one of the main leading causes of premature death worldwide ( 1 , 2 ) .
suicide rate is among the top three causes of death in people aged 1544 yr old ( 3 ) .
the rate of suicide ideation is reported to be highest among elderly ( 1 ) .
completed suicide is 5% to 10% of suicide attempts including one attempt in every 3 and one death from suicide in every 40 sec ( 5 ) . according to the report of iranian ministry of health and medical education in 2004 ,
suicide is the 13 cause of death ( 6 ) and the second cause of death from external causes of morbidity(7 ) , the rate of suicide attempt is 3 times in women , while , the rate of completed suicide is 4 times more common in men ( 8) . among iranian population at 2012 ,
3216 suicide attempts occurred including 6 suicide attempts per 100,000 populations with the highest rate in the second decade of life ( 9 , 10 ) .
classification is defined as an approach to determine a class for a new object applied using different methods such as data mining ( machine learning ) techniques ( 11 ) .
decision tree ( dt ) , k - nearest neighbor , logistic regression ( lr ) , naive bayes , c4.5 , support vector machine ( svm ) and linear classifier are among conventional classification methods ( 1214 ) .
classification methodsinclude two main steps : first , a training sample of the dataset is determined randomly to find the model and the second step tests the resulted model ( 12 ) .
according to the kind of dataset , different methods result in different accuracy of prediction .
the comparison among the methods can be applied using different criteria such as area under curve , which measures the accuracy of the prediction ( 13 ) . among different classification methods ,
lr is the most popular predicting the presence or absence of an attribute using covariates .
however , dt is preferable when there are predetermined set of attributes , the response is discrete and disjunctive and graphical results are required ( 15 ) . artificial neural network ( ann)as a non - linear , flexible , and general tool is capable of dealing with any sort of arbitrary function . support vector machine ( svm)is a kind of generalized linear models with a classification decision according to the value of the linear combination of features(16 , 17 ) .
this study aimed to determine factors putting people at a higher risk of completed suicide using different classification methods including lr , dt , ann and svm .
we used the dataset of a study conducted to predict risk factors of completed suicide in hamadan province , the west of iran , in 2010 ( 18 ) .
the dataset was based on a large population survey conducted in 2010 where all cases of suicide occurring in hamadan province from apr 2008 to mar 2010 were enrolled .
the presence of missing values was 17% in the dataset ; therefore , expectation maximization ( em ) algorithm was used for imputation . for this purpose ,
parameters in the equations imputed the missing values ( expectation ) , then , parameters were updated using all observations including the imputed ones ( maximization ) .
this procedure ended at the convergence ( 19 ) . to assess the fatal suicide , which is a binary variable ; using several risk factors , several classification methods were performed .
the data needed to be divided into two sub - sets where training sample of the dataset finds the model and the testing sample tests the resulted model .
the result derived from the learning sample ( 70% of cases ) was then evaluated by utilizing the test sample ( 30% of cases ) .
the applied methods were compared using sensitivity ( se ) , specificity ( sp ) , positive predicted value ( ppv ) , negative predicted value ( npv ) , accuracy ( acc ) , and the area under curve ( auc ) . logistic regression : lr is one of the most common applied classification methods in medical data analysis .
the model can be written as : 
 log(1)=+i=1kixi in this model , the xi s are the covariates to classify the response and the i s are the regression coefficients .
the log it , 
 log(1 ) 
 , indicates the odds ratio of classifying the response in category one than zero . artificial neural network : this method is an information - processing tool based on human brain performance . among different ann models , multilayer perceptron ( mlp ) is the most common used method , which includes layers as input , output , and hidden with nodes in each layer .
an activation function transforms the data in each layer to the latter one by introducing a degree of non - linearity .
input layer consist of all risk factors affecting the result of suicide , here including 6 variables .
the response variable is shown in the output layer with two nodes as the possible outcomes for suicide attempts . to find the best performance of the network
, a complicated nonlinear mapping between input and output layers is found using the number of nodes determined empirically in the hidden layer ( 20 ) .
support vector machine : a mapping function whether a classification or regression function is used in svms . to classify the result of suicide , a non - linear kernel function
is used in order to transform the input data to a high - dimensional space where the input data can be separated as well . radial basis function ( rbf ) kernel consists of two parameters trading off misclassification of training sample against simplicity of the decision surface ( cost parameter ) and to evaluate the influence degree of a training sample . choosing the kernel function as well as the parameters , acclaims svm as a flexible method , which the ability of the user can make the results more appealing . using maximum - margin hyper planes
, the classes will be best separated in the data . by contrasting two parallel hyper planes on each side of the separating hyper plane
, the minimum generalization error will be achieved when the distance between the hyperplanes takes place ( 21 ) .
the dt can be applied when the aim of the research is to identify or discriminate high - risk subjects .
the direction begins at the node and extends to the leaf , which connects the features .
the tree is a disjunction of these connections and these disjunctions separate the branch population into sets with the same likelihood of events . at each stage ,
the graphical feature presentation makes ease of interpretation and allowing to different alternatives ( 15 ) . to check the adequacy of the models ,
indices such as sensitivity , specificity , diagnostic accuracy ( da ) , positive predictive value ( ppv ) , negative predictive value ( npv ) , and the area under curve ( auc ) were calculated using the observed data as the gold standard .
the cochran - q test was used to check differences in proportion among methods . to assess
the association between the observed and predicted values several statistics were measures such as  coefficient , contingency coefficient , and kendall tau - b .
the dt can be applied when the aim of the research is to identify or discriminate high - risk subjects .
the direction begins at the node and extends to the leaf , which connects the features .
the tree is a disjunction of these connections and these disjunctions separate the branch population into sets with the same likelihood of events . at each stage ,
the graphical feature presentation makes ease of interpretation and allowing to different alternatives ( 15 ) . to check the adequacy of the models , indices such as sensitivity , specificity , diagnostic accuracy ( da ) , positive predictive value ( ppv ) ,
negative predictive value ( npv ) , and the area under curve ( auc ) were calculated using the observed data as the gold standard .
the cochran - q test was used to check differences in proportion among methods . to assess the association between the observed and predicted values several statistics were measures such as  coefficient , contingency coefficient , and kendall tau - b .
of 5414 people who attempted suicide 50.8% were male , 53.7% were married , 92.8% had no history of suicide , 47.3% and aged between 20 to 29 yr , 8.4% ( 457 subjects ) died of suicide .
the mean age of subjects was 26.3 yr ( 25.3 yr in females and 27.3 yr in males ) ranged from 10 to 90 yr . to identify the risk factors affecting completed suicide , lr , svm , dt and ann were performed to the data .
the test and train samples were composed of 1626 ( 30% ) and 3788 ( 70% ) cases , respectively .
the factors such as gender , job , age , education , marital status , and history of suicide attempt were considered as the explanatory variables for the performed methods . completed suicide was significantly associated with gender ( p<0.0001 ) and age ( p<0.05 ) in the lr model .
those aged between 2029 yr old was 3.14 times more likely to die from suicide than those aged 1019 yr ( table 1 ) . logistic regression model results among several ann models , the best model included one hidden layer and six hidden nodes .
hyperbolic tangent and softmax were the activation functions for hidden and output layers , respectively .
the importance of the variables is shown in fig . 1 presented by scores using sensitivity analysis .
the normalized importance of the variables in decision tree and artificial neural network to perform the svm model , gaussian radial basis function was used as the best non - linear kernel function for classifying the successful attempts .
this method showed a kernel parameter ( sigma ) of 0.24 , a cost parameter of 5 , and 2178 support vectors as the estimated parameters of the kernel function . in training sample ,
the weight assigned to the svm method was 11 for completed suicide and one for suicide attempt .
, the probability of completed suicide is presented according to the condition mentioned in its corresponding branch ( fig .
the classification tree with the probabilities of success for suicide attempts in each node a comparison of sensitivity , specificity , positive probability value , negative probability value , accuracy and the area under curve for training and testing sets of classification methods are shown in table 2 and fig .
cochran - q test resulted in differences between proportions in different methods ( p<0.001 ) .
multiple comparison adjusted for significance level was performed using mcnemar test which showed a significant difference in proportions of any two methods ( p<0.001 ) . to evaluate the association of the method predictions and
observed value of suicide attempts ,  coefficient , contingency coefficient , and kendall tau - b were performed which resulted in the best performance of svm in comparison to others ( table 3 ) .
the area under curve for the performed methods comparison of classification techniques lr : logistic regression , dt : decision tree , ann : artificial neural network , svm : support vector machine the association of performed methods with observed values lr : logistic regression , dt : decision tree , ann : artificial neural network , svm : support vector machine
in this study , gender was recognized as a significant risk factor for predicting completed suicide so did for age , and educational level in different applied methods . despite not being significant , marital status and history of suicide were the less important variables predicting completed attempts in dt and ann . in a study ,
furthermore , age of 2130 yr was associated with the highest rate of completed suicide . classifying educational level into three categories ( low - intermediate - high ) , the intermediate educated cases associated with the highest ratio of completed suicide .
moreover , they showed that married cases were more prone to die comparing to the single people ( 22 ) .
in other study , men selected high - risk methods of suicide and suicide related mortality rate was higher in men ( 23 ) . the rate of completed suicide was 2.5 in males compared to females .
moreover , age , occupation , marital status , and educational levelwere reported as significant risk factor for completed suicide ( 24 ) .
age was affecting significant variable for suicide with the highest age - specific suicide after 45 yr old in both japan and south korea ( 25 ) .
marital status was a significant risk factor resulted from the odds of completed suicide 2.77 for married cases ( 26 ) .
this study showed that among four statistically different classification methods including svm , lr , dt and ann for this data , svm had the best performance in classifying the risk factors associated with completed suicide . in spite of the least sensitivity in the testing sample and presence of unbalanced data ( 8.3% fatality in training sample ) , svm had the outperformance among mentioned methods and indicated the highest association between svm predicted and the observed values as well as the highest accuracy .
although , the assigned weight for the training sample was the best choice among all other assignments , the testing sample did not result in the same shape as the training sample because of different rates for fatal to non - fatal suicide attempts . to compare seven classification methods based on sample size and type of attributes , a sufficient number of records dt , svm , k - nearest neighborhood and c4.5 obtained a higher area under curve than lr , naive bayes , and linear classifier ( 27 ) . in another study ,
several classification methods including linear discriminant analysis , logistic regression , neural networks , support vector machines , classification trees and random forests were used to predict the dementia . despite of the highest specificity and lowest sensitivity of svm , this method had the highest accuracy among all different methods ( 28 ) .
the functioning of svm based methods against ann assessed in a study of analytical chemistry .
they recommended that the svm - based approach for practical application according to the robustness ( 29 ) .
conducting an empirical comparison between svm and ann , for classifying document - level sentiment , ann showed a better statistically significant prediction comparing to svm , even on the context of unbalanced data ( 30 ) .
finding predictive models for pre - operative diagnosis of rotator cuff tear , ann and lr were compared .
the study resulted in a higher predictive accuracy of ann than lr ( 31 ) . in a study , ann and dt
an outperformance for ann was found compared to dt where the mean absolute errors for the former were less than the latter one ( 32 ) . to classify the magnetic resonance imaging data in alzheimer s disease ,
different classification methods dt , ann , svm and orthogonal projections of latent structures ( opls ) were compared .
although there was no statistical difference among several methods , svm and opls outperformed slightly than dt and ann ( 33 ) . in a study that assessed differences between svm and lr , concluded that svm achieves a better performance in comparison to lr when fewer variables are included ( 34 ) .
to determine statistically the sex from craniometrists , three different methods lr , svm and linear discriminant analysis were compared .
the study showed a better reliability existed for males than females using all the methods while the results for svm had to be developed .
moreover , they found that lr was much more feasible than svm according to the choice about the kernel function and the parameters ( 35 ) . predicting the hospital mortality in critically ill patients with hematological malignancies , svm and lr
the comparing results were not statistically significant even though lr was resulted in a better predictive accuracy comparing to svm .
moreover , to predict the model using svm , only 4 variables were needed , whereas this number was 7 and 8 for lr ( 36 ) .
despite its limitations such as missingness in the data imputed , this study compared four different methods suggesting svm as the best classifier model , which may help the policymakers determining suicide risk factors .
svm had a better performance in classifying risk factors of completed suicide than other classification methods including dt , k - nearest neighbor , lr , naive bayes , c4.5 , svm , and linear classifier .
the flexibility of this method according to several choices for parameters and kernel function can make it as the first choice method for classification of such data .
ethical issues ( including plagiarism , informed consent , misconduct , data fabrication and/or falsification , double publication and/or submission , redundancy , etc . ) have been completely observed by the authors .
 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 ,  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 ,  coefficient , contingency coefficient , and kendall tau - b were 0.239 , 0.232 and 0.239 , respectively.conclusion:svm had the best performance to classify fatal suicide attempts comparing to dt , lr and ann . 
Introduction
Methods
Decision Tree
Results
Discussion
Conclusion
Ethical considerations