Depression Risk Prediction among Tech Employees using AdaBoost Tree

N. SreeDivya, U. Chaitanya, Chenemoni Vaishnavi and Pendyala Jaya Charan

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 3, 111-123

Abstract: Depression is slowly becoming a concern for employees working in the technology industry. Many people working in this industry tend to work for extended periods of time and are often faced with challenging tasks and tight deadlines. Overtime, this can lead to increased stress levels and may impact the mental health of employees. As a result, it is essential to identify the risk of depression at an early stage to help organizations offer better support and make the workplace a healthier place. This research applies a machine learning technique to investigate the risk of depression for employees in the technology industry. A survey conducted on technology professionals is analyzed using the AdaBoost (Adaptive Boosting) algorithm. The data set includes information about the demographic characteristics, working conditions, and psychological aspects that may affect mental health. Before constructing the predictive model, the data is preprocessed and structured in a manner that allows it to be used appropriately during the analysis. The results of the AdaBoost model are then compared with other popular classification techniques such as Logistic Regression, Random Forest, and Support Vector Machine. Based on the results acquired from the experiments, AdaBoost performs better in making predictions for the data set used in this research. The results indicate that machine learning techniques can be used to identify potential mental health risks at an earlier stage and help organizations make appropriate efforts to ensure the well-being of employees.

Keywords: Depression detection; machine learning; AdaBoost; employee mental health; classification; predictive analysis (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2612313
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i3:id:1995

DOI: 10.32628/CSEIT2612313

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