Depression prediction and therapy recommendation using machine learning technique
by K.G. Saranya; C.H. Babitha Reddy; M. Bhavyasree; M. Rubika; E. Varsha
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 8, No. 1/2, 2024

Abstract: The most common misconception around the world would be the definition of 'health'. A person is considered to be healthy as long as they are physically fine, but that is not true. A person's mental health is also equally important while considering a person's health status. This incomprehension towards mental health has taken lives of many people. Despite the government and many NGOs spreading awareness on mental health, there is still a lack of understanding of mental health symptoms, societal stigma, and proper resources and facilities prevent people from seeking help. Among the types of mental disorders, many psychiatrists have agreed that depression and addiction are the most common ones to cause a person's life. There are a couple of existing systems that aids in depression detection and therapy recommendation, but the major issue found in those systems would be inefficiency and high computational cost. In this paper work, a new approach has been proposed to identify depression using Reddit comments.

Online publication date: Tue, 19-Mar-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Systems Engineering (IJCSYSE):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com