Volume 21, Issue 56, July - December, 2024

Predicting the stress level of students using Supervised Machine Learning and Artificial Neural Network (ANN)

Suraj Arya¹, Anju^2♦, Nor Azuana Ramli³

¹Assistant Professor, Department of Computer Science and Information Technology, Central University of Haryana, Mahendergarh, India
²Research Scholar, Department of Computer Science & Information Technology, Central University of Haryana, Mahendergarh, India
³Senior Lecturer, Center for Mathematical Sciences, University Malaysia Pahang AI-Sultan Abdullah, 26300, Kuantan, Pahang, Malaysia

^♦Corresponding author
Research Scholar, Department of Computer Science & Information Technology, Central University of Haryana, Mahendergarh, India

ABSTRACT

Nowadays, the concept of stress is universally acknowledged. Many of us face situations that contribute to daily hassles, affecting professionals such as teachers, doctors, lawyers, journalists, and parents. University students are also encountering similar challenges. This study aims to identify the factors generating stress among students at Tribhuvan University Dharan in Nepal. We can predict and prevent stress at its early stages by analyzing these stress factors. This paper proposes various machine learning and deep learning models, including support vector machine (SVM), Random Forest, Gradient Boosting, AdaBoost, CatBoost, LightGBM, ExtraTree, XGBoost, logistic regression, K-nearest neighbor (KNN), Naive Bayes, decision tree, multi-layer perceptron (MLP), and artificial neural network (ANN). The Naive Bayes model achieved an accuracy of 90%, while SVM had the lowest test accuracy at 85.45%. The accuracy of these models improved with hyperparameter tuning. The key finding of this study is that the "academic period" is the most stressful time for students compared to other situations.

Keywords: Stress Prediction, Machine Learning, Random Forest, Naïve Bayes, Support Vector Machine, Artificial Neural Network

Indian Journal of Engineering, 2024, 21(56), e9ije1684

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DOI: https://doi.org/10.54905/disssi.v21i55.e9ije1684

Published: 3 August 2024