Detecting Stress Patterns in IT Employees Using Machine Learning Techniques

Abstract

Workplace stress is a significant concern,
particularly in the fast-paced and demanding
IT industry. Identifying and addressing stress
patterns in promoting employee well-being,
job happiness, and productivity requires an
effective IT workforce. This study focuses on
the use of machine learning methods to
identify stress tendencies in IT workers.
The paper proposes a novel approach that
leverages machine learning algorithms to
analyze various data sources, including
physiological signals, work-related data, and
self-reported indicators. By extracting
meaningful features and patterns from these
data sources, machine learning models can be
trained to accurately identify and classify
different stress levels in IT employees.
The research paper presents an extensive
literature review on stress detection and
highlights the gaps and limitations in existing
approaches. It then introduces a framework
that combines feature extraction, model
training, and classification techniques to build
robust stress detection models. The
framework encompasses methods like
random forests, support vector machines, and
deep learning models, tailored to the unique
characteristics of stress patterns in IT
employees.
To evaluate the proposed approach, realworld data from IT professionals are collected
and used for model training and validation.
Correctness, precision, recall, and F1-score
are a few of the assessment measures used to
evaluate how well the machine learning
models perform.
The results of this study add to the body of
information on spotting stress in the
workplace, specifically in the context of the IT
industry. By accurately detecting stress
patterns, organizations can proactively
address employee well-being and take
necessary measures to mitigate stress-related
issues. The proposed approach offers a
valuable tool for human resource
professionals and managers to monitor and
support the mental health and overall wellbeing of IT employees.