| Abstrakt: |
A recent report discusses research conducted by the University of Tehran on anxiety disorders among students. The study aimed to develop a data-driven optimization approach for identifying and providing therapy to students with symptoms of depression and anxiety. The proposed method uses data preprocessing, sentiment analysis, ensemble learning classifiers, hyperparameter optimization, and a rule-based dispatching system. The research found that the conventional approach to monitoring depression among students only detected 7 to 15% of cases, while the proposed strategy identified 44% of depressed and anxious students. This study provides valuable insights into managing post-disaster symptoms of depression and anxiety among students. [Extracted from the article] |
