Social and Behavioral Sciences Commons^™

Innovative Identification Of Substance Use Predictors: Machine Learning In A National Sample Of Mexican Children, Alejandro L. Vázquez, Melanie M. Domenech Rodríguez, Tyson S. Barrett, Sarah E. Schwartz Sarah.Schwartz@Usu.Edu, Nancy G. Amador Buenabad, Marycarmen N. Bustos Gamiño, María De Lourdes Gutiérrez López, Jorge A. Villatoro Velázquez

Psychology Faculty Publications

Machine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to utilize machine learning to identify high impact factors that best discriminate between substance users and non-users among a national sample (N = 52,171) of Mexican children (i.e., 5th, 6th grade; Mage = 10.40, SDage = 0.82). Participants reported information on individual factors (e.g., gender, grade, religiosity, sensation seeking, self-esteem, perceived risk of substance use), socioecological factors (e.g., neighborhood quality, community type, peer influences, parenting), and …