Depression Risk Prediction using Hybrid Deep Learning Algorithms

Abstract

Abstract: This research endeavors to address the critical challenge of early prediction of depression, a pervasive mental health disorder that often eludes timely detection. Recognizing the substantial impact of late-stage diagnosis on treatment outcomes, this study introduces a robust machine learning model that leverages diverse data sources to predict the likelihood of an individual experiencing depression. The proposed model under- goes meticulous development, involving extensive data collection and pre-processing to curate a comprehensive dataset encompassing various aspects of an individual’s life. Machine learning algorithms are then applied to analyze the dataset, extracting patterns and features indicative of depressive tendencies. To enhance the model’s predictive performance and overall efficiency, the suggested system advocates the use of hybrid algorithms, specifically combining Convolutional Neural Network (ConvNet) and Recurrent Neural Network (RNN) variants. This hybrid approach brings forth several advantages, including spa- tial feature extraction and a hierarchy of features. The integration of RNN variants with ConvNet facilitates effective extraction of spatial features from diverse data types such as text, images, videos, and other spatially structured data. Additionally, the CNN layers in the hybrid model learn hierarchical representations of features, capturing both low-level and high-level spatial patterns. This unique capability enhances the model’s understanding of complex structures within the input data. The proposed model is meticulously trained and validated using a diverse set of metrics to ensure its reliability and generalizability. The anticipated outcome of this project holds significant potential to revolutionize early intervention strategies, facilitating timely support for individuals at risk of depression. By amalgamating advanced machine learning techniques with a holistic approach to data analysis, this study contributes to the ongoing efforts aimed at enhancing mental health outcomes and alleviating the societal burden associated with depression.