MERGED COLLABORATIVE METHODS FOR AUTOMATED DETECTION OF DEPRESSION

Abstract

Depression rates have surged over the past century, yet many cases remain undetected despite
advancements in diagnosis. Automated detection methods offer potential solutions to this issue by identifying
individuals at risk. Effective feature representation and language analysis are crucial for understanding depression
detection. This project aims to enhance depression detection through text classifiers. Specifically, it compares two
sets of methods—hybrid and ensemble—to improve classification performance. The objective is to identify the most
effective approach for accurately detecting depression indicators in textual data. Text classifiers are employed and
trained for depression detection. Two sets of methods, hybrid and ensemble, are examined and compared. The
methodology involves effective feature representation and analysis of language use to enhance classification
accuracy. Various feature combinations and selection techniques are explored to optimize performance. The results
demonstrate that ensemble models outperform hybrid models in classifying depression indicators. The strength of
combined features underscores the importance of multiple feature combinations and proper selection. This finding
highlights the potential for improved depression detection through sophisticated ensemble methods. This
project advances depression detection methodologies, emphasizing the significance of ensemble approaches in
achieving better performance. The implications of this study extend to improved mental health screening and
intervention strategies, offering potential benefits for individuals at risk of depression. And also added an ensemble
method is employed which combines the predictions of multiple models, and the models are LSTM+GRU, Voting
Classifier(RF+AdaBoost), and Stacking Classifier(RF+MLP+LGBM+XGBoost), to enhance prediction accuracy.
Among these models, the Voting Classifier achieves 100% accuracy. Additionally, CNN, BERT, and XLNet
pretrained models are utilized for further analysis.