ArticleDiagnostics (Basel, Switzerland)2020
Accurate Diagnosis of Suicide Ideation/Behavior Using Robust Ensemble Machine Learning: A University Student Population in the Middle East and North Africa (MENA) Region.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Exploring suicidal thoughts among prospective university students: a study with applications of machine learning and GIS techniques.BMC psychiatry · 2025Article
- Identifying suicidal ideation in Chinese higher vocational students using machine learning: a cross-sectional survey.European archives of psychiatry and clinical neuroscience · 2025Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach.JMIR mental health · 2023Article
- Suicidality among university students in the Eastern Mediterranean region: A systematic review.PLOS global public health · 2023Article
- Decision Tree Algorithm for Visual Art Design in a Psychotherapy System for College Students.Occupational therapy international · 2022Article
- Validation of the Persian version of the Positive Mental Health Scale.BMC psychiatry · 2021Article
- Suicide among Ugandan university students: evidence from media reports for 2010-2020.BJPsych international · 2021Review
- A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining.Diagnostics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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.
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Registered trials
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