En-route for an Automatic early stress & non-stress detection analysis for supervised learning with Bayes’ theorem based on multimodal measurements

Abstract

<p>Chronic stress leads to mental health issues as well as other physical health issues such as cardiovascular disease. Excessive use of social media leads to Psychological stress. Around 20% of the world’s, among those aged 15 to 29, children and adolescents agonize from psychological illness through suicide is the second largest reason for impermanence. Mental Health Conditions may have a substantial effect on all aspects of life such as work performance or school, family and friend relationships, and socializing in society. A state of total well-being is one in which the mind is at ease and the body is upright. While much research has been done to determine the mind-body connection, a recent study found that heart health is strongly related to mental wellness. In a recent statement issued in the journal Circulation by the American Heart Association on March 9th, 2021, it was established that good psychological health can minimize the risk of heart disease. Today’s challenge is Covid 19 pandemic which caused anxiety, loneliness, fear of the pandemic, being infected, pessimism, etc. are the causes of major Heart diseases. People have turned to the Internet for help due to the lack of access to face-to-face management. The critical need for dependable and effective digital solutions is highlighted and designated by many available and widely accessible. We have proposed the framework for identifying the values of stress words and non-stress words using the Bayes theorem formula. If the words are stress words, then an action plan can be formulated thereby reducing the suicides and assuring the well-being of individuals. Finally, precision and accuracy are achieved with ISDF (Improved Stress Detection Framework) and Stress Detection using Bayes Theorem, unlike TensiStrength.</p>