Mental Stress Detection in University Students using Machine Learning Algorithms

Abstract

Mental stress is a major issue nowadays, especially among youngsters. The age that was considered once most carefree is now under a large amount of stress. Stress increase nowadays leads to many problems like depression, suicide, heart attack, and stroke. In this paper, we are calculating the mental stress of students one week before the exam and during the usage of the internet. Our objective is to analyze stress in the college students at different points in his life. The effect that exam pressure or recruitments stress has on the student which often goes unnoticed. We will perform an analysis on how these factors affect the mind of a student and will also correlate this stress with the time spent on the internet.The dataset was taken from Jaypee Institute of Information Technology and it consisted of 206 student’s data. Four classification algorithms Linear Regression, Naïve Bayes, Random Forest, and SVM is applied and sensitivity, specificity, and accuracy are used as a performance parameter. The accuracy and performance of data are further enhanced by applying 10-Fold Cross-Validation. The highest accuracy recorded was by Support Vector Machine (85.71%).