• Himanshu Kashyap
• Muskan Varshney
• Muskan

Depression, a leading cause of disability worldwide, significantly impacts individual well-being, social interactions, and productivity. Traditional diagnostic practices for depression are primarily subjective and rely on clinician assessments and self-reported symptoms, which can vary widely in accuracy. As a result, there is an urgent need for more objective, efficient, and scalable methods for early and reliable detection of depression. In this context, machine learning (ML) and neural networks (NNs) have shown great promise in revolutionizing mental health assessments by leveraging data from various sources, such as speech, text, physiological signals, and social media activities. This paper presents a comprehensive review of recent advancements in ML-based depression detection, with a particular focus on updated algorithms and deep neural networks. We explore innovative approaches using advanced neural architectures, such as transformers, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), which excel in processing complex data types like text, audio, and images. Transformer-based models like BERT and hybrid networks that combine CNNs and RNNs have recently gained popularity for their ability to capture contextual and temporal information, leading to improved accuracy in depression detection. Our analysis also includes the preprocessing and feature extraction techniques employed across various data modalities, discussing how different data sources—text, voice, facial expressions, and physiological metrics—contribute to the identification of depressive symptoms. Additionally, we review key datasets, performance metrics, and evaluation strategies used in the field to measure the effectiveness of these ML models. Challenges surrounding data quality, privacy, model interpretability, and ethical considerations are highlighted, along with potential solutions such as federated learning and explainable AI (XAI). The findings suggest that ML and NN models, especially with recent advancements, can complement traditional methods by providing objective insights and supporting early intervention strategies. However, the paper emphasizes that developing clinically viable tools will require overcoming several challenges, including enhancing data privacy, ensuring interpretability, and addressing ethical concerns. Future directions point toward personalized and adaptive models that leverage continuous data inputs, along with interdisciplinary collaborations to bridge the gap between technical innovation and clinical application. The paper ultimately advocates for a responsible integration of AI in mental health care, aiming to make depression detection more accurate, accessible, and proactive.

"Depression Detection Using Machine Learning and Neural Networks: An Updated Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.e214-e220, January-2025, Available :http://www.jetir.org/papers/JETIR2501427.pdf

"Depression Detection Using Machine Learning and Neural Networks: An Updated Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppe214-e220, January-2025, Available at : http://www.jetir.org/papers/JETIR2501427.pdf