A Review on Recent Machine Learning Applications for Addiction Disorders

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.