Prediction of Mental Disorder for employees in IT Industry
Sandhya P1, Mahek Kantesaria2

1Sandhya P, Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai (TamilNadu), India.

2Mahek Kantesaria, M.Tech, Department of Computer Science and Engineering, School of Computer Science and Engineering Vellore Institute of Technology, Chennai (TamilNadu), India.

Manuscript received on 04 April 2019 | Revised Manuscript received on 11 April 2019 | Manuscript Published on 26 April 2019 | PP: 374-376 | Volume-8 Issue-6S April 2019 | Retrieval Number: F61340486S19/19©BEIESP

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (

Abstract: Mental health is nowadays a topic which is most frequently discussed when it comes to research but least frequently discussed when it comes to the personal life. The wellbeing of a person is the measure of mental health. The increasing use of technology will lead to a lifestyle of less physical work. Also, the constant pressure on an employee in any industry will make more vulnerable to mental disorder. These vulnerabilities consist of peer pressure, anxiety attack, depression, and many more. Here we have taken the dataset of the questionnaires which were asked to an IT industry employee. Based on their answers the result is derived. Here output will be that the person needs an attention or not. Different machine learning techniques are used to get the results. This prediction also tells us that it is very important for an IT employee to get the regular mental health check up to tract their health. The employers should have a medical service provided in their company and they should also give benefits for the affected employees.

Keywords: Mental Health in IT, Machine Learning in Mental Health, Machine Learning, Mental Health.
Scope of the Article: Machine Learning