MLSCD – Stress Detection Analysis model using Stress Symptoms

Abstract

Fundamental analysis of the stress and its related elements signify that the stress could be attributed to various factors like the professional demands, too much of physical or mental exertion, unrealistic goals and ambitions, non-conducive working or family or social environment and various such conditions. The statistics revealed in many of the healthcare and the social science studies reflect on the alarming rates of the stress conditions. Though there were many effective models that were developed in the past, there are a very limited set of self-analysis-oriented models in stress assessment. This manuscript proposes a model MLSCD which relies on 15 features classified from three categories of symptoms (mind related, body-related, and behavior related). Considering behavior related as dependent with the other two as independent variable groups, the model is trained with SVM classifier for over 900 records and is tested for 300 records. The accuracy rate of 97% indicates that the model is potential and if iterative improvements can be made, it can be a pragmatic solution for real-time applications