Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network
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 o...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2016-10-01
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| Series: | Iranian Journal of Public Health |
| Subjects: | |
| Online Access: | https://ijph.tums.ac.ir/index.php/ijph/article/view/7869 |
| _version_ | 1818864637389570048 |
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| author | Payam AMINI Hasan AHMADINIA Jalal POOROLAJAL Mohammad MOQADDASI AMIRI |
| author_facet | Payam AMINI Hasan AHMADINIA Jalal POOROLAJAL Mohammad MOQADDASI AMIRI |
| author_sort | Payam AMINI |
| collection | DOAJ |
| description | 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 ANNwere 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.
Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN. |
| first_indexed | 2024-12-19T10:34:49Z |
| format | Article |
| id | doaj.art-c323996368634bed96cf5d9f298a8f8f |
| institution | Directory Open Access Journal |
| issn | 2251-6085 2251-6093 |
| language | English |
| last_indexed | 2024-12-19T10:34:49Z |
| publishDate | 2016-10-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Public Health |
| spelling | doaj.art-c323996368634bed96cf5d9f298a8f8f2022-12-21T20:25:39ZengTehran University of Medical SciencesIranian Journal of Public Health2251-60852251-60932016-10-014595018Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural NetworkPayam AMINI0Hasan AHMADINIA1Jalal POOROLAJAL2Mohammad MOQADDASI AMIRI3Dept. of Epidemiology & Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, IranDept. of Biostatistics & Epidemiology, Hamadan University of Medical Sciences, Hamadan, IranResearch Center for Health Sciences and Dept. of Biostatistics & Epidemiology, Hamadan University of Medical Sciences, Hamadan, IranDept. of Biostatistics & Epidemiology, Hamadan University of Medical Sciences, Hamadan, IranBackground: 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 ANNwere 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. Conclusion: SVM had the best performance to classify fatal suicide attempts comparing to DT, LR and ANN.https://ijph.tums.ac.ir/index.php/ijph/article/view/7869SuicideSupport vector machineNeuralnetworksLogistic regressionDecision treeClassification |
| spellingShingle | Payam AMINI Hasan AHMADINIA Jalal POOROLAJAL Mohammad MOQADDASI AMIRI Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network Iranian Journal of Public Health Suicide Support vector machine Neuralnetworks Logistic regression Decision tree Classification |
| title | Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network |
| title_full | Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network |
| title_fullStr | Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network |
| title_full_unstemmed | Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network |
| title_short | Evaluating the High Risk Groups for Suicide: A Comparison of Logistic Regression, Support Vector Machine, Decision Tree and Artificial Neural Network |
| title_sort | evaluating the high risk groups for suicide a comparison of logistic regression support vector machine decision tree and artificial neural network |
| topic | Suicide Support vector machine Neuralnetworks Logistic regression Decision tree Classification |
| url | https://ijph.tums.ac.ir/index.php/ijph/article/view/7869 |
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