International Journal of Advanced Engineering Application

Analyzing Various Machine Learning Classifiers for the efficient Prediction of Student Mental Stress

Author(s):Priyanka Gupta 1, Dr. Anil Pandit2

Affiliation: 1,2 Research Scholar, GNA University, Phagwara, Punjab, India

Page No: 1-12

Volume issue & Publishing Year: Volume 2 Issue 5 ,May-2025

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.17674316

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Article Indexing:

Abstract:
A prevalent societal issue that affects people nowadays is mental stress. Stress is typically felt when one feels that the amount
of pressure or demand is more than one's ability to handle it. A person's thoughts, actions, emotions, and interpersonal
communication can all be impacted by mental health problems. The major issues that student faces now a days that will
suffer their mental health are Depression, Addiction, Anxiety, Eating Disorders, Substance Misuse and Suicidal Intent.
Some Students also suffers from a Huge Academic Pressure. It might be from their own mind for gaining more & more in
their Academics or might be from Parental Pressure. Accurate analysis and prediction of stress patterns may be possible
with the use of machine learning techniques and enabling prompt responses. With an emphasis on the function of machine
learning models, the influence of physiological and behavioral characteristics, this paper explores the important facets of
mental stress detection. The search was conducted on several databases (IEEE, Scopus, Elsevier, and Web of Science).The
topmost objective of the paper is to analyze various algorithms that are used to predict the level of stress among an individual.
This Review paper is based on the analysis of various approaches and finally gives the most appealing among all. Random
Forest & Gradient Boosting are the best algorithm with topmost accuracy that has been used in various papers and also
helps in accurately predicting the level of stress among the individual.

Keywords: K Nearest Neighbor, Random Forest, Naive Bayes, Regression, Decision Tree, Support vector machine, Gradient Boosting

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