Stress Detection in IT Professionals using Machine Learning

Stress Detection in IT Professionals using Machine Learning

Vishwagna Praharshitha1, Dr. Gifta Jerith G2, Dr Thayabba Katoon3

1Student, MTech in ML&DL, Malla Reddy University

2Assistant Professor-AIML, Malla Reddy University

3HoD-AIML, Malla Reddy University

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Abstract - In today's fast-paced technology landscape, stress management is becoming increasingly important, especially among IT professionals. The work environment in the IT industry is often characterized by long hours, tight deadlines, and high expectations, which can lead to elevated stress levels. Unchecked stress not only impacts the health and well-being of professionals but also affects productivity and job satisfaction. This study aims to predict the stress levels of IT professionals using machine learning techniques, thereby aiding in proactive stress management. We utilize a range of features indicative of work stress, including Heart Rate, Skin Conductivity, Hours Worked, Number of Emails Sent, and Meetings Attended. These features provide a comprehensive view of both the physiological and work-related factors that contribute to stress. The application of machine learning in this context serves as an innovative approach to an increasingly pertinent issue. By leveraging the power of data analytics, this model aims to provide actionable insights for both individuals and organizations. Individuals can use these predictions for self-monitoring and early intervention, while organizations can utilize them to identify high-stress environments or roles, thereby allocating resources or interventions more effectively. Our preliminary results indicate a strong correlation between the chosen features and stress levels, demonstrating the viability of using machine learning for stress prediction in IT professionals. This study stands as a crucial step towards a more data-driven approach to mental health and well-being in the workplace.

Key Words:  :  Randomforest, adaboost, extratree, Stress Detection, IT Professionals, Machine Learning, Stress Analysis, Mental Health