Prediction models for suicide attempts among adolescents using machine learning techniques
Jae Seok Lim 1, Chan-Mo Yang 2, 3, Ju-Won Baek 4, Sang-Yeol Lee 2, Bung-Nyun Kim 3*
1Department of Oral and Maxillofacial Surgery, Chungbuk National University Hospital, Cheongju 28644, South Korea, 2Department of Psychiatry, School of Medicine, Wonkwang University, Iksan 54538, South Korea, 3Division of Child and Adolescent Psychiatry, Department of Psychiatry, Graduate School of Medicine, Seoul National University, Seoul 03080, South Korea, 4Dental Clinic Center, Chungbuk National University Hospital, Cheongju 28644, South Korea
Received: March 25, 2021; Revised: May 18, 2021; Accepted: May 27, 2021; Published online: May 27, 2021.
© The Korean College of Neuropsychopharmacology. All rights reserved.

Objective: Suicide attempts (SAs) in adolescents are difficult to predict although it is a leading cause of death among adolescents. This study aimed to develop and evaluate SA prediction models based on six different machine learning (ML) algorithms for Korean adolescents using data from online surveys.
Methods: Data were extracted from the 2011–2018 Korea Youth Risk Behavior Survey (KYRBS), an ongoing annual national survey. The participants comprised 468,482 nationally representative adolescents from 400 middle and 400 high schools, aged 12 to 18. The models were trained using several classic ML methods and then tested on internal and external independent datasets; performance metrics were calculated. Data analysis was performed from March 2020 to June 2020.
Results: Among the 468,482 adolescents included in the analysis, 15,012 cases (3.2%) were identified as having made an SA. Three features (suicidal ideation, suicide planning, and grade) were identified as the most important predictors. The performance of the six ML models on the internal testing dataset was good, with both the area under the receiver operating characteristic curve (AUROC) and area under the precision–recall curve (AUPRC) ranging from 0.92 to 0.94. Although the AUROC of all models on the external testing dataset (2018 KYRBS) ranged from 0.93 to 0.95, the AUPRC of the models was approximately 0.5.
Conclusions: The developed and validated SA prediction models can be applied to detect high risks of SA. This approach could facilitate early intervention in the suicide crisis and may ultimately contribute to suicide prevention for adolescents.
Keywords: Adolescent, Suicide, Attempted Suicide, Machine learning