A Deep Learning Framework for Stress and Depression Detection

Mohammad, Nihaal Naazim (2024) A Deep Learning Framework for Stress and Depression Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

Stress and depression are some of the leading mental health disorders affecting the society in today’s world. These conditions are still a major concern to public health, thus there is a need for prevention and control measures. This work focuses on training Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on stress and depression public datasets to predict mental health outcomes. CNNs were deployed on structured data for extracting spatial features while, RNNs analyzed temporal features extracted from behavioral measurements. The results show that RNNs exceeded CNNs, achieving an impressive 99% accuracy in stress prediction and 79%accuracy in depression analysis. Meanwhile, CNNs demonstrated strong performance in classifying structured data. RNNs were more effective in processing sequential data and handling finer distinctions in categorization. These findings underscore the potential of deep learning to enhance traditional analytical methods and provide scalable, efficient tools for mental health assessment. Future research will focus on integrating multimodal data and developing hybrid models to further improve the models’ performance and applicability in real-world scenarios.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Anu UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning R Medicine > RA Public aspects of medicine > RA790 Mental Health B Philosophy. Psychology. Religion > Psychology > Stress (Psychology)
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 14:14
Last Modified:	03 Sep 2025 14:14
URI:	https://norma.ncirl.ie/id/eprint/8750

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