A Comprehensive Approach to Substance Abuse Rehabilitation and De-Addiction Interventions

Substance addiction is a widespread global issue, affecting individuals and society for the long term. Traditional methods of rehabilitation ignore the unique requirements of each patient, and treatment outcomes are accordingly inconsistent. This research highlights the role of machine learning (ML) in addressing these problems by allowing individualized treatment. By considering relapse risk, predicting the treatment success, and optimizing intervention techniques, ML allows evidence-based methods of recovery from addiction. This research explains fundamental ML approaches, including Random Forests, Support Vector Machines, and Neural Networks, and demonstrates their application to treatment in the substance abuse domain. Our research highlights the role of ML in revolutionizing rehabilitation processes by allowing accurate early detection, personalized therapies, and optimized patient outcomes, thereby paving the way for more efficient deaddiction models.