Constant contributions of Machine Learning (ML) technology in health sciences has extended to solve addiction disorders problems, whether to detect symptoms or predict risks and treatment outcomes. This article presents an updated review related to the application of ML techniques for addiction disorders, the selected works covered substance addiction (N=18 studies) and non-substance addiction (N=3 studies), and were divided into three categories prognosis, diagnosis, and predicting treatment success. To provide strong evidence about the potential of ML methods to accelerate early prevention and intervention, ultimately aiming to pave the way for further applications of ML approaches in this field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.