Analyzing the Impact of Digital Transformation on Mental Health of IT Professionals Using Machine Learning Methods

Driven by artificial intelligence and automation, the rising speed of digital transformation presents fresh difficulties for the mental health of IT workers. With specific attention to stress and burnout, this study investigates the effects of technostress, fast upskill demands, workload intensification, and remote work on the psychological health of IT workers. Using a mixed-methods approach, this study combines a custom-built dataset created from a focused survey of IT professionals with the publicly accessible Open Sourced Mental Illness (OSMI) dataset. While including explainable artificial intelligence (XAI) approaches to improve model interpretability and trustworthiness, the combined data is utilized to create machine learning models for estimating workplace stress levels. This study seeks to enable early identification and focused interventions by identifying important stress predictors including workload, role type, managerial support, and automation anxiety. By emphasizing the most important elements influencing personal stress estimates, XAI approaches guarantees transparency. The results have pragmatic ramifications for HR and organizational leaders using data-driven mental health approaches to create better, more resilient digital workplaces.