Details

Autor(en) / Beteiligte

• Imtinan Uddin, J.M.
• Fatema, Kaniz
• Kumar Dhar, Pranab

Titel

Depression Risk Prediction among Tech Employees in Bangladesh using Adaboosted Decision Tree

Ist Teil von

• 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 2020, p.135-138

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Depression is a major depressive disorder. It is a psychological problem that hampers a person's daily life and decreases productivity. It is becoming a severe problem in our world. People of any age can be depressed. Despite having a flourishing tech industry in Bangladesh, cases of depression among tech employees are highly seen, which eventually leads to an unwanted situation in an employee's professional career and personal life. Depressed employees neither can concentrate on work nor are they able to be productive. This kind of incident is becoming common in the tech industry. For this reason, we are facing problem to achieve our desired goal in the tech industry. People can be depressed for various reasons. Many risk factors that contribute to depression include family problems, work pressure, lack of physical movement, drug abuse, etc. This research aims to predict the depression risk of a tech employee and determine the root cause of depression so that it becomes easier to treat depression at an early stage. To predict depression risk, the authors have used the Adaboosted decision tree. Using this Adaptive boosting technique on the standard decision tree approach, the authors have achieved better accuracy compared to the standard decision tree approach. In adaptive boosting, errors of previous models are corrected. The features of the dataset that were used to train and test the machine learning model was determined by a psychiatrist by rigorous analysis. The features were selected considering risk factors that contribute to depression. The data used for this research was collected under the direct supervision of a psychiatrist and technology expert. The Author's primary purpose of this research is to predict depression at a preventive stage so that necessary measures can be taken to treat depression among tech employees and to avoid unwanted situations.