Advanced Text-Based Depression Detection Using Machine Learning Techniques

Early and correct diagnose of depression are critical when planning for mental health intervention. In this work, we propose a new method for studying depression via text analysis through state-of-the-art machine learning and deep learning techniques. In particular, we examine the performance of Classical CNN and LGBM algorithms for depression predictors using text data. CNNs are good at capturing intricate features in unstructured text and on the other hand, LGBM is one of the best performing models trained on structured data. Presentation methods effectiveness in depression detection is assessed by conducting a comparative analysis of two dissimilar approaches, which are examined in this research. Two models are trained on training data, and hyperparameter tuning is done to improve the predictiveness of each. The models will be evaluated with performance metrics; for example: accuracy, precision recall, F1-score and others in order to understand how well the different models could deal with text. This study attempts to find the most proficient technique in text-based depression detection, specifically using CNN and LGBM for comparison, a study that provides potential scalable solutions for health monitoring. These findings support the development of early intervention strategies, and may be used by mental health professionals to identify cases through automated and personalized text messages.