| Sumario: | Background: Research indicates that marijuana is the most used illegal substance in states where this substance remains criminalized, with increasing use among young adults. There are important gender differences in substance use behavior. Specific risk variables implying different marijuana use behavior by gender are unclear. Method: This was a data mining study using machine learning. Random Forest, a machine learning algorithm, was used to build prediction models and a Minimum Redundancy Maximum Relevance feature (variable) selection to identify important risk variables by gender in young adults who abuse marijuana. The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) (American Psychiatric Association, 2013) was used to identify current marijuana abusers in the National Survey on Drug Use and Health survey data. Results: A total of 22,411 participants were identified as marijuana abusers (male = 10,619; female = 11,792). The 2,651 variables were included in the Minimum Redundancy Maximum Relevance feature selection process. A prediction model built with 1% of the entire variables (n=27) showed best performance (ROC= 0.9617) in a male group and one built with 1.2% of the variables (n=32) showed the best performance (ROC= 0.9600) in a female group. Both groups used multiple substances besides marijuana. A male group showed use of substances that boost pleasure or relieve pain while a female group showed use of anxiety-relieving substances. Conclusion: The best performing marijuana addiction prediction models and identified risk variables by gender could be used for developing new effective marijuana prevention, treatment, and rehabilitation programs for men and women who overuse this substance.
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