ISSN: 2319-7293
Sara Mohebtash*, Behnam Sedghi
The objective of this review was to assess and analyze the application of machine learning techniques in predicting drug users. By examining the current research and literature, this review aims to identify the effectiveness, challenges and advancements in using machine learning algorithms to predict drug use behaviors. The intention was to provide insights into the potential uses of machine learning in predicting drug users and to highlight significant trends and future directions in this field. The utilization of machine learning to predict drug use has the potential to revolutionize the field of substance abuse prevention and intervention. Furthermore, machine learning algorithms can process vast amounts of data, enabling the identification of patterns that may not be apparent to human experts, resulting in more precise and effective interventions for substance abuse prevention. Future research should concentrate on enhancing algorithms, integrating multiple data sources and developing personalized interventions based on predictive models. Machine learning has emerged as a promising solution for addressing the complex issue of drug use. To develop a comprehensive search strategy, targeted databases and relevant search terms were used to identify research articles that specifically investigated the application of machine learning in predicting drug use. The review revealed that machine learning algorithms have exhibited remarkable effectiveness in predicting drug users by leveraging data sources such as behavioral patterns, electronic health records and social media. These algorithms have demonstrated a high degree of accuracy in identifying individuals at risk of drug use and have the potential to enhance intervention strategies. The potential of machine learning to predict drug use lies in its capacity to transform the field of substance abuse prevention and intervention. Future research should focus on refining algorithms, integrating multiple data sources and developing personalized interventions based on predictive models.
Published Date: 2025-04-19; Received Date: 2024-07-25