Abstract

Background: Existing research using machine learning to investigate alcohol use among adolescents has largely neglected peer influences and tended to rely on models which selected predictors based on data availability, rather than being guided by a unifying theoretical framework. In addition, previous models of peer influence were typically estimated by using traditional regression techniques, which are known to have worse fit compared to the models estimated using machine learning methods.

Methods: Addressing these limitations, we use three machine-learning algorithms to fit a theoretical model of social interactions in alcohol consumption. The model is fit to a large, nationally representative sample of U.S. school-aged adolescents and accounts for various channels of peer influence.

Results: We find that extreme gradient boosting is the best performing algorithm in predicting alcohol consumption. After the algorithm ranks, the explanatory variables by their importance in classification, previous year drinking status, misperception about friends’ drinking, and average actual drinking among friends are the most important predictors of adolescent drinking.

Conclusions: Our findings suggest that an effective intervention should focus on school peers and adolescents’ perceptions about drinking norms, in addition to the history of alcohol use. Our study may also increase interest in theory-driven selection of covariates for machine-learning models.

摘要

背景：现有的使用机器学习来调查青少年饮酒情况的研究在很大程度上忽略了同伴的影响，并且倾向于依赖基于数据可用性选择预测因子的模型，而不是由统一的理论框架指导。此外，以前的同伴影响模型通常是使用传统回归技术估计的，与使用机器学习方法估计的模型相比，已知这些技术具有更差的拟合度。

方法：解决这些限制，我们使用三种机器学习算法来拟合酒精消费中社会互动的理论模型。该模型适用于具有全国代表性的美国学龄青少年的大型样本，并考虑了同伴影响的各种渠道。

结果：我们发现极端梯度提升是预测酒精消耗的最佳算法。在算法排名后，解释变量在分类中的重要性、上一年饮酒状况、对朋友饮酒的误解以及朋友之间的平均实际饮酒量是青少年饮酒最重要的预测因素。

结论：我们的研究结果表明，除了饮酒史外，有效的干预措施还应关注学校同龄人和青少年对饮酒规范的看法。我们的研究也可能会增加对机器学习模型协变量的理论驱动选择的兴趣。