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

Aug 5, 2026

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Stress is widely recognized as a daily mental pressure affecting many professions and increasingly university students as well. This study targets student stress at Tribhuvan University Dharan in Nepal by identifying factors that generate stress and enabling early prediction and prevention. Multiple machine learning and deep learning models are proposed and compared, including SVM, Random Forest, gradient boosting variants, logistic regression, KNN, Naive Bayes, decision tree, MLP, and ANN. Naive Bayes reaches 90% accuracy, while SVM achieves 85.45%.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

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Abstract

Introduction

Stress concept and definition

Stress types: acute and chronic

Stress factors and management approaches

Structure of the paper

Related Works

Indian journal of Engineering To Cite: Arya S, Anju, Ramli NA. Predicting the Stress level of students using Supervised Machine Learning and Artificial Neural Network (ANN). Indian Journal of Engineering, 2024, 21, e9ije1684 doi: Author Affiliation: 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, India *Corresponding Author Research Scholar, Department of Computer Science & Information Technology, Central University of Haryana, Mahendergarh, India Email: [anju24sanga@gmail.com](anju24sanga@gmail.com) [Peer-Review History](Peer-Review History) [Received: 02 May 2024](Received: 02 May 2024) Reviewed & Revised: 06/May/2024 to 22/July/2024 Accepted: 26 July 2024 Published: 3 August 2024 Peer-Review Model External peer-review was done through double-blind method. Indian Journal of Engineering pISSN 2319–7757; eISSN 2319–7765 © The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution License 4.0 (CC BY 4 .0) ., which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. To view a copy of this license, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) . DISCOVERY SCIENTIFIC SOCIETY Predicting the stress level of students using Supervised Machine Learning and Artificial Neural Network (ANN) Suraj Arya1, Anju2*, Nor Azuana Ramli3 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. 1. INTRODUCTION Stress is a state of mind in which a person feels pressured to perform daily routine activities. This phenomenon is evident across various sectors, including but not limited to offices, universities, hospitals, and others. Sometimes, it is obvious, but generally, it results from higher expectations and low passion, unrealistic workloads, insecure jobs, community violence, and examinations. Professionals like teachers, doctors, lawyers, journalists, parents, and others go through stressful situations. Even students are not spared from the stress. Students are the future of every country, so it is essential to analyze the factors responsible for stress among students. Thus, by earlier detection of these factors, stressful situations can be ignored or controlled. According to the World Health Organizati

FAQs

What is the main goal of the study?

The study aims to identify factors generating stress among students at Tribhuvan University Dharan and to predict and prevent stress in early stages using machine learning and deep learning models.

Which models are evaluated for student stress prediction?

The paper evaluates multiple ML and deep learning models, including SVM, Random Forest, gradient boosting methods (e.g., XGBoost, LightGBM, CatBoost), logistic regression, KNN, Naive Bayes, decision tree, MLP, and ANN.

Which model shows the best and lowest test accuracy?

Naive Bayes achieves the highest test accuracy at 90%, while SVM has the lowest test accuracy at 85.45%.

Aug 5, 2026

Upload by Oliver