AI-BASED PREDICTION OF ADOLESCENT SUICIDAL TENDENCIES

Abstract

Artificial intelligence (AI) and machine learning (ML) technologies offer new opportunities for identifying and predicting adolescent suicidal tendencies. This study explores the development of an AI-based system for suicide risk assessment using ML algorithms and data-driven analysis. We looked at different ways to guess who might be at risk for suicide, like using computer programs that think like brains and also decision trees, to see how things people do, think, and how they relate to others might play a role. The proposed system integrates data from multiple sources, such as social media activity, medical history, and psychological assessments, to enhance prediction accuracy. We also talk about what's right and wrong, keeping data safe, and how AI tips might fit into mental health help. The findings suggest that AI-based predictive models can significantly improve early intervention strategies, offering a proactive approach to adolescent mental health care.