Prediction of Depression in Young Adults Using Supervised Learning Algorithm

Title

Prediction of Depression in Young Adults Using Supervised Learning Algorithm

Creator

Chakraborty A.; Shukla S.

Description

Over the years, mental health has achieved an essential role in the pertinent development of a human being, and a large part of the population is affected by it. The most commonly affected community being college-going students, and the most common disorders being Anxiety and Depression. Depression is a leading cause of suicide in individuals, where suicide is the second most prevailing reason for death among 1529-year-olds. This study aims to identify the different reasons and other factors associated with depression to predict and determine whether an individual faces depressive disorders. For this research purpose, the most appropriate classifier is selected. The absolute accuracy of the proposed model is 91.17%, i.e., the model can correctly predict whether an individual has depression 91.17% of the time. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-290, pp. 446-460.

Date

2021-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification; Depression; Oversampling; Random Forest Classifier; Recursive Feature Elimination

Coverage

Chakraborty A., Christ University, Bangalore, India; Shukla S., Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981164485-6

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-16-4486-3_50

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115086670&doi=10.1007%2f978-981-16-4486-3_50&partnerID=40&md5=7a4a6a1b20bc400b14ff93eeb0ffe9a6