Multivariable machine learning prediction of risky alcohol use in contemporary youth
An ensemble learning approach appears to have good predictive ability of risky alcohol use among a contemporary cohort of young Australians. It underscores the complex interplay of individual, familial and social factors occurring across childhood and adolescence that influences risky alcohol use in early adulthood.
Authors
- Rachel Visontay (ORCID: https://orcid.org/0000-0002-0693-1012)
- Philip James Clare (ORCID: https://orcid.org/0000-0002-2009-7386)
- Lucinda Grummitt (ORCID: https://orcid.org/0000-0003-4466-7731)
- Tim N Slade (ORCID: https://orcid.org/0000-0002-1725-9188)
- Louise Birrell (ORCID: https://orcid.org/0000-0002-1335-1382)
Institutions
- The University of Sydney (AU)
- UNSW Sydney (AU)
Publication Details
- Journal
- Addiction
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1111/add.70145
- Primary Topic
- Substance Abuse Treatment and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Sydney
- NSW Ministry of Health
- NSW Health
- Medical Research Council
- National Health and Medical Research Council