Addiction
-
Comparative Study
Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.
The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modeling of adolescent alcohol use. The current study (a) compared the performance of seven machine-learning algorithms to predict different levels of alcohol use in mid-adolescence and (b) used a cross-cultural cross-study scheme in the training-validation-test process to display the predictive power of the best performing machine-learning algorithm. ⋯ Computerized screening software shows promise in predicting the risk of alcohol use among adolescents.