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Multivariable machine learning prediction of risky alcohol use in contemporary youth.

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Title: Multivariable machine learning prediction of risky alcohol use in contemporary youth.
Authors: Grummitt, Lucinda (AUTHOR), Visontay, Rachel (AUTHOR), Clare, Philip (AUTHOR), Slade, Tim (AUTHOR), Birrell, Louise (AUTHOR)
Source: Addiction. Dec2025, Vol. 120 Issue 12, p2404-2412. 9p.
Subjects: Alcoholism risk factors, Risk assessment, Risk-taking behavior, Prediction models, Secondary analysis, Research funding, Scientific observation, Descriptive statistics, Longitudinal method, Machine learning, Data analysis software, Algorithms, Adolescence, Children, Adults
Abstract: Background and aims: Risky alcohol use in young adulthood is a significant public health concern. Understanding the predictors of risky drinking during this period is essential for prevention. This study aimed to measure the predictive accuracy of ensemble machine learning and identify the most important predictors of risky alcohol use in early adulthood. Design and setting: Secondary analysis of the Longitudinal Study of Australian Children, an Australian national longitudinal cohort study. Participants: A total of 4983 children, aged 4–5 years in 2004 (Wave 1), followed up for eight waves (to age 18/19 in 2018). Measurements: Risky alcohol use was measured at age 18 and defined as more than 10 standard drinks per week, as per Australian National guidelines. Predictors from multiple domains—sociodemographic, adolescent substance use, adolescent mental health and behaviours, parental mental health and substance use, school factors, peer influences, parenting practices and parental stress—were included, measured from Wave 1 to 7. The SuperLearner package in R was used to test a series of models [regularised regression (LASSO, ridge and elastic net), random forest and kernel support vector machine (SVM)] using nested 10‐fold cross‐validation to identify the overall predictive ability of the model (measured by area under the curve; AUC) and the most important predictors of risky alcohol use across childhood and adolescence. Predictor importance was derived by normalising algorithm‐specific scores per fold, weighting them by SuperLearner coefficients and aggregating across folds to rank predictors by mean weighted importance on a scale of 0 to 1 (higher scores indicating greater importance). Findings The ensemble model showed good prediction on the test set, with an AUC of 0.792, a slight improvement over any single algorithm (AUC = 0.783 for the best performing individual algorithm). The most important predictors were weekly drinking at the previous wave (mean weighted importance 0.999), lifetime cannabis use (0.446), lifetime parent financial stress (0.420), identifying as female (0.365), identifying as male (0.344; compared with a reference category of gender diverse), lifetime attention deficit hyperactivity disorder (0.248), pre‐natal alcohol exposure (0.248), housing insecurity (0.243), religious involvement (0.238) and parent alcohol use problems (0.215). Conclusions: 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. [ABSTRACT FROM AUTHOR]
Copyright of Addiction is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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DbLabel: Psychology and Behavioral Sciences Collection
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PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: Multivariable machine learning prediction of risky alcohol use in contemporary youth.
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  Data: <searchLink fieldCode="AR" term="%22Grummitt%2C+Lucinda%22">Grummitt, Lucinda</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Visontay%2C+Rachel%22">Visontay, Rachel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clare%2C+Philip%22">Clare, Philip</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Slade%2C+Tim%22">Slade, Tim</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Birrell%2C+Louise%22">Birrell, Louise</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Addiction%22">Addiction</searchLink>. Dec2025, Vol. 120 Issue 12, p2404-2412. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Alcoholism+risk+factors%22">Alcoholism risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Risk-taking+behavior%22">Risk-taking behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+analysis%22">Secondary analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+observation%22">Scientific observation</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis+software%22">Data analysis software</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescence%22">Adolescence</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background and aims: Risky alcohol use in young adulthood is a significant public health concern. Understanding the predictors of risky drinking during this period is essential for prevention. This study aimed to measure the predictive accuracy of ensemble machine learning and identify the most important predictors of risky alcohol use in early adulthood. Design and setting: Secondary analysis of the Longitudinal Study of Australian Children, an Australian national longitudinal cohort study. Participants: A total of 4983 children, aged 4–5 years in 2004 (Wave 1), followed up for eight waves (to age 18/19 in 2018). Measurements: Risky alcohol use was measured at age 18 and defined as more than 10 standard drinks per week, as per Australian National guidelines. Predictors from multiple domains—sociodemographic, adolescent substance use, adolescent mental health and behaviours, parental mental health and substance use, school factors, peer influences, parenting practices and parental stress—were included, measured from Wave 1 to 7. The SuperLearner package in R was used to test a series of models [regularised regression (LASSO, ridge and elastic net), random forest and kernel support vector machine (SVM)] using nested 10‐fold cross‐validation to identify the overall predictive ability of the model (measured by area under the curve; AUC) and the most important predictors of risky alcohol use across childhood and adolescence. Predictor importance was derived by normalising algorithm‐specific scores per fold, weighting them by SuperLearner coefficients and aggregating across folds to rank predictors by mean weighted importance on a scale of 0 to 1 (higher scores indicating greater importance). Findings The ensemble model showed good prediction on the test set, with an AUC of 0.792, a slight improvement over any single algorithm (AUC = 0.783 for the best performing individual algorithm). The most important predictors were weekly drinking at the previous wave (mean weighted importance 0.999), lifetime cannabis use (0.446), lifetime parent financial stress (0.420), identifying as female (0.365), identifying as male (0.344; compared with a reference category of gender diverse), lifetime attention deficit hyperactivity disorder (0.248), pre‐natal alcohol exposure (0.248), housing insecurity (0.243), religious involvement (0.238) and parent alcohol use problems (0.215). Conclusions: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Addiction is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1111/add.70145
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 9
        StartPage: 2404
    Subjects:
      – SubjectFull: Alcoholism risk factors
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Risk-taking behavior
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Secondary analysis
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      – SubjectFull: Research funding
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      – SubjectFull: Scientific observation
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Longitudinal method
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      – SubjectFull: Machine learning
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      – SubjectFull: Data analysis software
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      – SubjectFull: Algorithms
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      – SubjectFull: Adolescence
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      – SubjectFull: Children
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      – SubjectFull: Adults
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      – TitleFull: Multivariable machine learning prediction of risky alcohol use in contemporary youth.
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              M: 12
              Text: Dec2025
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              Y: 2025
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