<![CDATA[Self-harm among teenagers has become a critical public health concern, often associated with underlying psychological distress and undetected behavioral patterns. This study explores the development and effectiveness of early intervention strategies for self-harm prevention by utilizing artificial intelligence (AI)-driven behavior tracking systems. By leveraging machine learning algorithms and real-time monitoring tools, the system identifies high-risk behaviors and emotional cues from digital footprints, such as social media activity, messaging patterns, and wearable data. The research adopts a mixed-methods approach, combining behavioral data analysis with expert validation from mental health professionals to enhance predictive accuracy and ethical compliance. Results indicate that AI-based tracking significantly improves the ability to flag early warning signs, enabling timely counseling and intervention. The study contributes to the emerging field of digital mental health by proposing a scalable, proactive solution for youth well-being, while emphasizing the importance of privacy, consent, and multidisciplinary collaboration in its implementation.]]>
Copyrights © 2025
