Stress Analysis Using Machine Learning Techniques

Abstract

Stress is a psychological or physical sentiment of trouble. This can rise up out of any occasion or feeling that causes you to feel disturbed, irate or anxious. Stress is a response to a threat or demand from your body. It is a mental condition which diminishes rest quality and influences each part of life. Stress is a significant factor affecting lives of people. It is therefore imperative to advise an individual about their unhealthful way of life and even to make the person in question aware of any intense condition. Individuals feel the adverse effects of misery every year and few get sufficient consideration on time. In this way, distinguishing and dealing with stress is fundamental before it gets into a serious issue. Stress management problem has gotten huge consideration in related research networks because of a more extensive acknowledgment of potential issues brought about by constant pressure and because of recent advancements in technologies that give non-intrusive methods for consistently gathering target estimations to screen the stress of an individual. Therefore, stress analysis is considered crucial at a beginning phase since stress-related variations may rise the possibilities of strokes, cardiovascular failures, discouragement, and hypertension. This can likewise initiate self-destructive speculation among the sufferers of this neurological state. For both medical practitioners and people with disabilities, computer-aided treatment became a way forward. Machine learning revolution's recent growth has proved significant for medicalprognosis. Neurological methods may leverage this approach. It can be possible through the power of machine learning techniques to distinguish such generic form of stress behaviors from several regular tasks. The purpose of this research work is to underline a quantitative evaluation of approaches that can be used to define and measure stress in general. This study attempts to include a clearer and more reliable comparison of various methods and featuresin order to recommend the best technique and feature from all existing methods and features. This theoretical work also provides a study of the methods used in detecting and predicting stress.