How machine learning is used to study addiction in digital healthcare: A systematic review

Abstract

Long-term use of drugs can sometimes result in brain damage that greatly affects a person's psychology and sometimes become indecent. This paper examines psychological disorders caused by substance abuse by examining literatures that involved machine learning (ML) models. The brain imaging, behavioural kinematics, and memory analysis are studied to gain insights of substance use and its disorder. Review analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In order to help better screen, diagnose and monitor such disorders, ML identifies early onset of substance intake as predictors of disorders. The study measures identified in the articles (N=26) illustrate the exclusive use of ML to bring out insights of substance use disorders. Brain-related factors, behavioural phenotypes, and functional differentiation of the brain can express a great deal about disorders. Findings also identify the insights into various research levels, classification techniques, performance measures, challenges, and future directions related to use of ML. Random forests models are largely used for better performance. In addition, the diversity of interviews, questionnaires, brain imaging and the latest digital tools is part of this review. A longitudinal study with clinical validation could open up new models to explore substance use disorders.

Keywords

• Machine learning
• Alcohol addiction
• MRI
• Random forest
• Depression
• Risk