Risk Prediction Models in Adolescents Using ML Techniques

Abstract: Adolescents are at high risk for mental health issues, including depression, due to factors such as social media exposure, peer pressure, and behavioral influences. Early detection of at-risk individuals enables timely intervention and support. This study presents a hybrid depression detection model that integrates Naïve Bayes, TF-IDF, and emoji sentiment analysis to enhance classification accuracy. The approach utilizes textual and emoji-based sentiment cues extracted from social media data to identify depressive tendencies. The model is evaluated against traditional classifiers, demonstrating improved precision, recall, and F1-score. A comparative analysis of different machine learning techniques highlights the model’s effectiveness in detecting depression-related expressions. The findings emphasize the potential of AI-driven approaches in mental health monitoring, particularly for adolescents, by leveraging real-time social media interactions. This research contributes to the growing field of automated mental health assessment, offering a scalable and data-driven solution for early intervention. Future work will explore deep learning and multimodal analysis to further refine depression detection and provide more comprehensive insights into mental health patterns