A Novel Stress Identification based on the Health Aspects of an Individual using Strategical Learning Methodology

The modern human experience increasingly includes high levels of stress. According to the World Health Organization, stress is a mental illness that can negatively affect physical health. The effects of stress and depression can affect anyone, anywhere. There is tension in everyone's life. Stress is a major indicator of emotional well-being. Every area of a person's life, including their emotions, thoughts, and behaviors, are impacted by stress. Analyzing physiological signs is a traditional way to detect stress, according to earlier study. These readings come from sensors that are worn by the person being monitored. Researchers have attempted to detect stress by studying physiological information with traditional machine learning methods. The accuracy of the outcomes varies widely, from around 50% to almost 90%. Traditional machine learning methods have the issue of requiring handcrafted features. Accuracy drops if attributes are recognized wrongly. This research study has created a hybrid deep learning model to detect staff stress. With the intention of enhancing life quality, this study showed how deep neural networks may be used to create reliable, continuous, and noninvasive techniques for stress detection and emotion categorization. As they enable ready libraries for stress prediction, these ready tools and languages make it straightforward to predict stress. Data science approaches are efficient in processing the training datasets and can accurately and quickly predict the human stress.