Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region
Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine le...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/10/11/956 |
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| author | Azam Naghavi Tobias Teismann Zahra Asgari Mohammad Reza Mohebbian Marjan Mansourian Miguel Ángel Mañanas |
| author_facet | Azam Naghavi Tobias Teismann Zahra Asgari Mohammad Reza Mohebbian Marjan Mansourian Miguel Ángel Mañanas |
| author_sort | Azam Naghavi |
| collection | DOAJ |
| description | Suicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine learning approach. Therefore, stability feature selection and stacked ensembled decision trees were employed in this classification problem. A total of 573 university students responded to a battery of questionnaires. Three-fold cross-validation with a variety of performance indices was sued. The proposed diagnostic system had excellent balanced diagnosis accuracy (AUC = 0.90 [CI 95%: 0.86–0.93]) with a high correlation between predicted and observed class labels, fair discriminant power, and excellent class labeling agreement rate. Results showed that 23 items out of all items could accurately diagnose suicide ideation/behavior. These items were psychological problems and how to experience trauma, from the demographic variables, nine items from Post-Traumatic Stress Disorder Checklist (PCL-5), two items from Post Traumatic Growth (PTG), two items from the Patient Health Questionnaire (PHQ), six items from the Positive Mental Health (PMH) questionnaire, and one item related to social support. Such features could be used as a screening tool to identify young adults who are at risk of suicide ideation/behavior. |
| first_indexed | 2024-03-10T14:49:03Z |
| format | Article |
| id | doaj.art-9f84fa89432446b69d6f7a4e76500385 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T14:49:03Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-9f84fa89432446b69d6f7a4e765003852023-11-20T21:09:25ZengMDPI AGDiagnostics2075-44182020-11-01101195610.3390/diagnostics10110956Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) RegionAzam Naghavi0Tobias Teismann1Zahra Asgari2Mohammad Reza Mohebbian3Marjan Mansourian4Miguel Ángel Mañanas5Department of Counseling, Faculty of Education and Psychology, University of Isfahan, Azadi Sq, Isfahan 8174673441, IranDepartment of Clinical Psychology and Psychotherapy, Ruhr-Universität Bochum, 44787 Bochum, GermanyDepartment of Counseling, Faculty of Education and Psychology, University of Isfahan, Isfahan 8174673441, IranDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N5A9, CanadaBiomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, SpainBiomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), 08028 Barcelona, SpainSuicide is one of the most critical public health concerns in the world and the second cause of death among young people in many countries. However, to date, no study can diagnose suicide ideation/behavior among university students in the Middle East and North Africa (MENA) region using a machine learning approach. Therefore, stability feature selection and stacked ensembled decision trees were employed in this classification problem. A total of 573 university students responded to a battery of questionnaires. Three-fold cross-validation with a variety of performance indices was sued. The proposed diagnostic system had excellent balanced diagnosis accuracy (AUC = 0.90 [CI 95%: 0.86–0.93]) with a high correlation between predicted and observed class labels, fair discriminant power, and excellent class labeling agreement rate. Results showed that 23 items out of all items could accurately diagnose suicide ideation/behavior. These items were psychological problems and how to experience trauma, from the demographic variables, nine items from Post-Traumatic Stress Disorder Checklist (PCL-5), two items from Post Traumatic Growth (PTG), two items from the Patient Health Questionnaire (PHQ), six items from the Positive Mental Health (PMH) questionnaire, and one item related to social support. Such features could be used as a screening tool to identify young adults who are at risk of suicide ideation/behavior.https://www.mdpi.com/2075-4418/10/11/956suicidetraumatic eventsscreening tooluniversity studentsmachine learningMiddle East and North Africa (MENA) |
| spellingShingle | Azam Naghavi Tobias Teismann Zahra Asgari Mohammad Reza Mohebbian Marjan Mansourian Miguel Ángel Mañanas Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region Diagnostics suicide traumatic events screening tool university students machine learning Middle East and North Africa (MENA) |
| title | Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region |
| title_full | Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region |
| title_fullStr | Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region |
| title_full_unstemmed | Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region |
| title_short | Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region |
| title_sort | accurate diagnosis of suicide ideation behavior using robust ensemble machine learning a university student population in the middle east and north africa mena region |
| topic | suicide traumatic events screening tool university students machine learning Middle East and North Africa (MENA) |
| url | https://www.mdpi.com/2075-4418/10/11/956 |
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