Human Stress Indicator Using Machine LearningTechnique and Exhortation based on Health Parameters

Abstract

Stress, often known as stressors, is a psychological or emotional state brought on by difficult or inevitable situations. Understanding human stress levels is vital to preventing negative life experiences. There may be connections between sleep-related difficulties and a range of psychological, social, and medical conditions. The aim is to look into the empirical identification of human stress levels by applying algorithmic techniques with health data. After data pre- processing, a few algorithmic approaches were utilized to assess stress levels, which were categorized from low to high: Multilayer Perception, Random Forest, Support Vector Machine, Decision Trees, Na ̈ıve Bayes, and Logistic Regression. This strategy made it possible to compare methods and find the most precised one.