SCRS Conference Proceedings on Intelligent Systems
Prediction and Analysis of Recurrent Depression Disorder: Deep Learning Approach
Authors: Anagha Pasalkar and Dhananjay Kalbande
Publishing Date: 21-09-2021
ISBN: 978-93-91842-08-6
Abstract
"Mental illness, such as depression, is rampant and has been shown to affect a person’s physical health. With the growth in artificial intelligence (AI) various methods are introduced to assist mental health care providers, including psychiatrists to construct proper decisions based on patient’s chronicle information including sources like medical records, behavioral data, social media usage, etc. Many researchers have come up with various strategies that include various machine learning algorithms for data analysis of depression. Although there have been less attempts previously to perform the same task without making the use of pre-classified data and Word-Embedding optimization Approach. For these reasons, this study aims to identify the deep formation of the neural network among a few selected structures that will successfully complement natural language processing activities to analyze and predict depression."
Keywords
Machine Learning; Deep Learning; MDD; RCNN; RNN; CNN; DL; Word-Embedding Optimization
Cite as
Anagha Pasalkar and Dhananjay Kalbande, "Prediction and Analysis of Recurrent Depression Disorder: Deep Learning Approach", In: Raju Pal and Praveen Kumar Shukla (eds), SCRS Conference Proceedings on Intelligent Systems, SCRS, India, 2021, pp. 35-49. https://doi.org/10.52458/978-93-91842-08-6-3