Revealing suicide risk of young adults based on comprehensive measurements using decision tree classification

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dc.contributor.authorNiu, Wenbang
dc.contributor.authorFeng, Yi
dc.contributor.authorXu, Shicun
dc.contributor.authorWilson, Amanda
dc.contributor.authorJin, Yu
dc.contributor.authorMa, Zhihao
dc.contributor.authorWang, Yuanyuan
dc.date.acceptance2024-04-05
dc.date.accessioned2024-08-22T07:53:26Z
dc.date.available2024-08-22T07:53:26Z
dc.date.issued2024-09
dc.description.abstractPredicting suicide risk based on risk and protective factors is a critical and complex endeavor. In this study, we combined insights from comprehensive aetiological theories on suicide with the methodological strengths of machine learning techniques. Our primary objectives were twofold: a) to identify hazardous feature combinations that characterize a high risk of suicide, and b) to enhance our understanding of the potential interactions between risk and protective factors related to suicide. We established an interpretable decision tree model to classify young adults at high risk of suicide, utilizing fifty-five variables covering distal, developmental, proximal, and social context factors from a large-scale cross-sectional survey (N = 88,214). The results highlighted the significance of variables such as self-compassion and non-suicidal self-injury (NSSI), and the accumulation of depressive symptoms, medium-to-low self-compassion, and a history of NSSI as substantial indicators of heightened suicide risk. This study serves as a valuable reference for the clinical identification of individuals at risk of suicide.
dc.exception.reasonnot deposited within three months of acceptance
dc.funderNo external funder
dc.identifier.citationNiu, W. et al. (2024) Revealing suicide risk of young adults based on comprehensive measurements using decision tree classification. Computers in Human Behavior, 158, 108272
dc.identifier.doihttps://doi.org/10.1016/j.chb.2024.108272
dc.identifier.issn0747-5632
dc.identifier.urihttps://hdl.handle.net/2086/24134
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofComputers in Human Behavior
dc.titleRevealing suicide risk of young adults based on comprehensive measurements using decision tree classification
dc.typeArticle
oaire.citation.volume158

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