Machine learning models identify key predictors of driving under the influence of alcohol or cannabis

The frequency of substance use, early age of initiation, and cannabis-related memory impairments are among the primary factors contributing to driving under the influence, according to a new analysis using machine learning. Impaired driving is known to be influenced by a range of behavioral, demographic, and contextual factors.