Developing a data-driven predictive model for substance abuse prevention among youth using behavioral analytics
1 University of North Carolina Chapel Hill, USA.
2 Independent Researcher, USA.
Review
International Journal of Frontline Research in Science and Technology, 2024, 05(02), 001–018.
Article DOI: 10.56781/ijsrst.2024.5.2.0036
Publication history:
Received on 18 September 2024; revised on 2274 October 2024; accepted on 29 October 2024
Abstract:
Substance abuse among youth presents a critical public health challenge, necessitating innovative approaches for early intervention and prevention. This study proposes the development of a data-driven predictive model that leverages behavioral analytics to identify youth at risk of substance abuse. The model utilizes data from multiple sources, including social media activity, school performance records, and mental health screenings, to analyze behavioral patterns that may indicate a predisposition toward substance misuse. The core methodology integrates machine learning algorithms to process and analyze large datasets, uncovering correlations between specific behaviors and the likelihood of substance abuse. Predictive features such as changes in social engagement, academic performance fluctuations, and indicators of emotional distress are identified and incorporated into the model to enhance its accuracy. By applying supervised learning techniques, the model is trained to recognize patterns in historical data, allowing it to make predictions about future substance use risks. Furthermore, the model's design emphasizes real-time monitoring and adaptability, enabling health professionals and educators to receive timely alerts and intervene early when behavioral warning signs are detected. The application of behavioral analytics in this context offers a more proactive, personalized approach to prevention, targeting at-risk individuals before they develop harmful substance use habits. In addition to its predictive capabilities, the model also provides actionable insights into effective intervention strategies. By identifying the most influential behavioral factors, it informs tailored prevention programs that address specific risk behaviors among youth. These findings can support policymakers and healthcare providers in developing data-driven, evidence-based prevention initiatives that better allocate resources to high-risk populations.
Keywords:
Data-Driven Predictive Model; Substance Abuse Prevention; Youth; Behavioral Analytics; Machine Learning; Early Intervention; Risk Factors; Public Health; Real-Time Monitoring; Personalized Prevention Strategies.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0