-
1
Mental disorders like depression, anxiety, and stress are increasingly common among young adults, impacting their academic and social functioning.
-
2
Traditional assessment methods for mental health are resource-intensive and often fail to provide timely support, highlighting the need for scalable alternatives.
-
3
The proposed hybrid framework combines population-level modeling with individual-specific adaptation to enhance personalized mental health predictions.
-
4
The hybrid approach achieved lower RMSE values for depression, anxiety, and stress compared to population-level models, indicating improved prediction accuracy.
-
5
Integrating population-level knowledge with individual adaptation offers a better balance between generalization and personalization in mental health assessment.
