DCR-HMM: Depression detection based on Content Rating using Hidden Markov Model

Ansari, H and Vijayvergia, A and Kumar, K (2019) DCR-HMM: Depression detection based on Content Rating using Hidden Markov Model. In: 2018 Conference on Information and Communication Technology, CICT 2018, 26 - 28 October 2018, Jabalpur.

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Abstract

Depression is a mental health issue that once used to be experienced by grandparents and great-grandparents, is so common today even among the youth. Over the years, methods have been developed to counter this mental health issue. But the biggest problem that is being faced is people find it hard to admit that they are suffering from depression. Although many people admit it and take measures, a majority of people find it hard to admit, especially students. Depression is common nowadays among the student but several social issues like peer pressure, gossips etc. make it hard for them to accept that they are suffering from depression. In our paper, we have introduced a novel approach to detect depression based on the content rating by the subject using Hidden Markov Model (HMM). A series of content is provided to the subject and based on whether the subject reacts to it skip it, we predict whether the subject is depressed or not. The experiments demonstrate that the proposed DCR-HMM model leads to very outcomes. Based on the acceptance of the state of mind by tested individuals, our model has acquired an accuracy of 95.6 when tested on 450 individuals.

Item Type:	Conference Paper
Publication:	2018 Conference on Information and Communication Technology, CICT 2018
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Content ratings; HMM models; Mental health; Peer pressure; Social issues, Hidden Markov models
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	08 Aug 2022 05:05
Last Modified:	08 Aug 2022 05:05
URI:	https://eprints.iisc.ac.in/id/eprint/75472

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