Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents.

Autor: Negriff S; Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, United States of America., Dilkina B; Department of Computer Science, University of Southern California, Los Angeles, California, United States of America., Matai L; Department of Computer Science, University of Southern California, Los Angeles, California, United States of America., Rice E; Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, California, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Sep 21; Vol. 17 (9), pp. e0274998. Date of Electronic Publication: 2022 Sep 21 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0274998
Abstrakt: Objective: This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors.
Method: Data were from a Time 4 (Mage = 18.22) of longitudinal study of the effects of maltreatment on adolescent development (n = 350; CW = 222; non-CW = 128; 56%male). Marijuana use in the past 12 months (none versus any) was obtained from a single item self-report. Risk factors entered into the model included mental health, parent/family social support, peer risk behavior, self-reported risk behavior, self-esteem, and self-reported adversities (e.g., abuse, neglect, witnessing family violence or community violence).
Results: The ML approaches indicated 80% accuracy in predicting marijuana use in the CW group and 85% accuracy in the non-CW group. In addition, the top features differed for the CW and non-CW groups with peer marijuana use emerging as the most important risk factor for CW youth, whereas externalizing behavior was the most important for the non-CW group. The most important common risk factor between group was gender, with males having higher risk.
Conclusions: This is the first study to examine the shared and unique risk factors for marijuana use for CW and non-CW youth using a machine learning approach. The results support our assertion that there may be similar risk factors for both groups, but there are also risks unique to each population. Therefore, risk factors derived from normative populations may not have the same importance when used for CW youth. These differences should be considered in clinical practice when assessing risk for substance use among adolescents.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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