Multivariable machine learning prediction of risky alcohol use in contemporary youth.
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 impo...
| Publicado en: | Addiction Vol. 120; no. 12; pp. 2404 - 2413 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189104075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189104075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: Dec2025 vid: 120 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 189104075 186670709 189104075 189104075 10.1111/add.70145 189104075 ppf: 2404 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multivariable machine learning prediction of risky alcohol use in contemporary youth. aug: au: Grummitt, Lucinda Visontay, Rachel Clare, Philip Slade, Tim Birrell, Louise affil: The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, Australia sug: subj: Alcohol Abuse Risk Factors Alcohol Abuse Risk Factors Alcohol Abuse Risk Factors Risk Taking Behavior Risk Assessment Machine Learning Prediction Models Human Secondary Analysis Funding Source Descriptive Statistics Data Analysis Software Male Female Adolescence Young Adult Algorithms Nonexperimental Studies Prospective Studies Child Adolescent: 13-18 years Child: 6-12 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|