Using Decision Trees to Identify Salient Predictors of Cannabis-Related Outcomes

ABSTRACT

Cannabis use continues to escalate among emerging adults and college attendance may be a risk factor for use. Severe cases of cannabis use can escalate to a cannabis use disorder, which is associated with worse psychosocial functioning. Predictors of cannabis use consequences and cannabis use disorder symptom severity have been identified; however, they typically employ a narrow set of predictors and rely on linear models. Machine learning is well suited for exploratory data analyses of high-dimensional data. This study applied decision tree learning to identify predictors of cannabis user status, negative cannabis-related consequences, and cannabis use disorder symptoms. Undergraduate college students (N = 7000) were recruited from nine universities in nine states across the U.S. Among the 7 trees, 24 splits created by 15 distinct predictors were identified. Consistent with prior research, one’s beliefs about cannabis were strong predictors of user status. Negative reinforcement cannabis use motives were the most consistent predictors of cannabis use disorder symptoms, and past month cannabis use was the most consistent predictor of probable cannabis use disorder. Typical frequency of cannabis use was the only predictor of negative cannabis-related consequences. Our results demonstrate that decision trees are a useful methodological tool for identifying targets for future clinical research.

KEYWORDS:

• Machine learning
• decision tree
• recursive partitioning
• cannabis
• college students

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website

Notes

1. IRBs (listed alphabetically): Colorado State University, Old Dominion University, University of Buffalo, University of California, Los Angeles, University of Central Florida, University of Houston, University of Memphis, University of New Mexico, University of Washington.

Additional information

Funding

This work was supported by the National Institute on Alcohol Abuse and Alcoholism and the National Institute on Drug Abuse of the National Institutes of Health [AA018108,AA023233,AA028712]. The content is the sole responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.