TY  - JOUR
PY  - 2020//
TI  - Accurate diagnosis of suicide ideation/behavior using robust ensemble machine learning: a university student population in the Middle East and North Africa (MENA) Region
JO  - Diagnostics (Basel, Switzerland)
A1  - Naghavi, Azam
A1  - Teismann, Tobias
A1  - Asgari, Zahra
A1  - Mohebbian, Mohammad Reza
A1  - Mansourian, Marjan
A1  - Mañanas, Miguel Ángel
SP  - e956
EP  - e956
VL  - 10
IS  - 11
N2  - 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. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 2075-4418
UR  - http://dx.doi.org/10.3390/diagnostics10110956
ID  - ref1
ER  -