Abstract:
This research presents a comprehensive exploration of depression identification
methodologies, employing a diverse array of classification algorithms, including Logistic
Regression, Random Forest, Gradient Boosting, Multilayer Perceptron (MLP), and Long
Short-Term Memory Networks (LSTMs). Utilizing a dataset comprising textual
expressions, traditional machine learning models are juxtaposed against a deep learning
paradigm, aiming to discern intricate patterns indicative of depression. Noteworthy
outcomes emerge, with Logistic Regression and Random Forest achieving commendable
accuracies of 95.60% and 95.69%, respectively. The study introduces an LSTM model,
showcasing its potential in text-based depression identification, yielding an accuracy of
73.79%. Beyond quantitative assessments, the research delves into the societal impact,
ethical considerations, and sustainability of the proposed models. Recognizing the
significance of mental health awareness, this study contributes valuable insights into
algorithmic frameworks for depression detection, fostering a nuanced understanding of
their applicability, ethical considerations, and societal implications. The findings not only
provide a comprehensive comparison of state-of-the-art models but also underscore the
need for responsible deployment and sustainable practices in leveraging machine learning
for mental health applications. As I navigate the complexities of mental health analysis,
this research seeks to offer a holistic perspective, emphasizing ethical considerations and
societal implications while opening avenues for future research and advancements in the
domain.
