Stress has become increasingly common among IT employees due to the high demands and long working hours of their jobs. It can manifest itself in a variety of ways, including physical and mental exhaustion, anxiety, and depression. As such, it is important to detect stress in IT employees in order to provide them with the necessary resources and support to cope with it. Machine learning can be used to detect stress in IT employees by analyzing various data points from their daily activities. This could include data gathered from surveys, physiological recordings, and even digital footprints. By using algorithms to analyze the data, machine learning can identify patterns that indicate stress and provide an accurate assessment of an individual’s stress level. One way to use machine learning for stress detection is to create a model that utilizes data from surveys and physiological recordings to identify stress-related behaviors. The model would monitor an individual’s activity and analyze it for signs of stress, such as increased heart rate, changes in sleep patterns, and irregular levels of fatigue. The model would then provide the individual with feedback and recommendations on how to better cope with the stress they are experiencing. Another way to use machine learning for stress detection is to utilize digital footprints in combination with survey data.

stress, IT employees, working hours, resources, data point, foot prints, machine learning