The intersectionality of gambling addiction recovery and mental illness: A machine learning approach

Abstract

A machine learning algorithm identified that struggling with substance use, impulse control, education, and resourcefulness was the significant barriers to improvement from problem gambling in state-funded behavioral health services. Interestingly, White adults were more likely to be improved from problem gambling than their peers of color. The machine learning-based gambling addiction recovery model could be a promising approach to detect the intersection of race/ethnicity, behavioral health challenges, and their improvement from problem gambling. It could eventually be a basis for developing a gambling addiction recovery model for adults with needs for gambling addiction treatment at the initial assessment. Such a relationship study will support the development of an efficient mental health and gambling recovery model.