HYBRID MODEL FOR DEPRESSION DETECTION USING DEEP LEARNING

Abstract: Millions of people are suffering from mental illness due to unavailability of early treatment and services for depression detection. It is the major reason for anxiety disorder, bipolar disorder, sleeping disorder, depression and sometimes it may lead to self-harm and suicide. Thus, it is a very challenging task to recognize people who are suffering from mental health disorders and provide them treatments as early as possible. In this proposed system, we are developing a hybrid model for depression detection using deep learning algorithms, by analysing textual features and audio features of patient's responses. Proposed system consists of a textual CNN model in which a CNN model is trained with only text features and an audio CNN model in which CNN model is trained with only audio features. System uses model parameters such as precision, F1-score, recall and support are found for evaluation of models.

Keywords: Depression Detection, Deep Learning, Hybrid Model, Textual Features, Audio Features, Convolutional Neural Network (CNN), Early Intervention, Mental Health Disorders, Machine Learning, Data Collection, Feature Extraction, Precision, F1-Score, Recall, Support, Mental Health Support, Text Analysis, Audio Analysis, System Architecture, System Requirements Cite: Arencheruvu Dinesh, Arsh Ahmed, Hasan Shifan, Ms. R Lalitha, "HYBRID MODEL FOR DEPRESSION DETECTION USING DEEP LEARNING", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13355.