Please use this identifier to cite or link to this item:
Longitudinal Study: LSAC
Title: Machine learning-based prediction for self-harm and suicide attempts in adolescents
Authors: Su, Raymond
John, James Rufus
Lin, Ping-I
Publication Date: 29-Aug-2023
Pages: 115446
Keywords: suicidal behaviour
mental health
artificial intelligence
random forest
Abstract: This study aimed to use machine learning (ML) models to predict the risk of self-harm and suicide attempts in adolescents. We conducted secondary analysis of cross-sectional data from the Longitudinal Study of Australian Children dataset. Several key variables at the age of 14-15 years were used to predict self-harm or suicide attempt at 16-17 years. Random forest classification models were used to select the optimal subset of predictors and subsequently make predictions. Among 2,809 participants, 296 (10.54%) reported an act of self-harm and 145 (5.16%) reported attempting suicide at least once in the past 12 months. The area under the receiver operating curve was fair for self-harm (0.7397) and suicide attempt (0.7220), which outperformed the prediction strategy solely based on prior suicide or self-harm attempt (AUC: 0.6). The most important factors identified were similar, and included depressed feelings, strengths and difficulties questionnaire scores, perceptions of self, and school- and parent-related factors. The random forest classification algorithm, an ML technique, can effectively select the optimal subset of predictors from hundreds of variables to forecast the risks of suicide and self-harm among adolescents. Further research is needed to validate the utility and scalability of ML techniques in mental health research.
DOI: 10.1016/j.psychres.2023.115446
Research collection: Journal Articles
Appears in Collections:Journal Articles

Show full item record

Page view(s)

checked on Sep 25, 2023
Google icon

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.