Comparative Analysis Of Various Types Of Stress Test Used In Machine Learning
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Abstract
Stress is state of worry or tension caused by a difficult situation. Sometimes stress may be positive or negative. The stress changes the behavior of person. Stress is an action in which body reacts to any kind of threat. Stress automatically effects on a person’s life, family life, and social life. Stress is detected using various factors. Various supervised, and unsupervised machine-learning algorithm are being used to detect stress efficiently and effectively among a huge population. Supervised learning algorithms are used for mental stress detection, however the accuracy of the algorithm depends upon the training data. This paper reports, the various tests required for detecting stress level like Perceived Stress Scale (PSS), Holmes and Rahe Stress Scale/SRRS, Daily Hassles Scale/ Hassles and uplift scales(HUPS), Depression, Anxiety, and Stress Scale (DASS), Perceived Stress Reactivity Scale (PSRS) test.
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