Mental Stress Detection Using Ensemble Machine Learning Method

Abstract

Abstract: These days, it's common for people to feel mild to severe psychological strain in an array of circumstances. A person can benefit from a reasonable amount of stress, but excessive stress has a detrimental influence on mental health and raises the risk of thoughts of suicide if left unchecked for an extended period of time.Long-term stress has been shown to be related to physical health issues. It is essential to be able to identify stress at a young age and assist individuals in realising and resolving it before significant harm is done since a growing number of people are experiencing stress. Machine learning approaches have become quite prominent in research in the area of stress detection. In this study, we focused on identifying stress management using machine learning techniques on the mental stress dataset, which was acquired from a 2014 survey that evaluates views on mental stress and the frequency of mental stress sickness in the IT sector.We observed that ADA-Boost had the highest accuracy rate of all the machine learning models (81.75%, AUC of 0.8185), using the provided dataset to train and test them.