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Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)

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 th...

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Main Authors: Arya, Suraj, Anju, ., Nor Azuana, Ramli
Format: Article
Language:English
Published: Discovery Scientific Society 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/42513/1/e9ije1684.pdf
http://umpir.ump.edu.my/id/eprint/42513/
https://doi.org/10.54905/disssi.v21i55.e9ije1684
https://doi.org/10.54905/disssi.v21i55.e9ije1684
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spelling my.ump.umpir.425132024-09-06T03:44:28Z http://umpir.ump.edu.my/id/eprint/42513/ Predicting the stress level of students using supervised machine learning and artificial neural network (ANN) Arya, Suraj Anju, . Nor Azuana, Ramli QA75 Electronic computers. Computer science 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. Discovery Scientific Society 2024-07 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42513/1/e9ije1684.pdf Arya, Suraj and Anju, . and Nor Azuana, Ramli (2024) Predicting the stress level of students using supervised machine learning and artificial neural network (ANN). Indian journal of Engineering, 21 (56). pp. 1-24. ISSN 2319-7765. (Published) https://doi.org/10.54905/disssi.v21i55.e9ije1684 https://doi.org/10.54905/disssi.v21i55.e9ije1684
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Arya, Suraj
Anju, .
Nor Azuana, Ramli
Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)
description 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.
format Article
author Arya, Suraj
Anju, .
Nor Azuana, Ramli
author_facet Arya, Suraj
Anju, .
Nor Azuana, Ramli
author_sort Arya, Suraj
title Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)
title_short Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)
title_full Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)
title_fullStr Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)
title_full_unstemmed Predicting the stress level of students using supervised machine learning and artificial neural network (ANN)
title_sort predicting the stress level of students using supervised machine learning and artificial neural network (ann)
publisher Discovery Scientific Society
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42513/1/e9ije1684.pdf
http://umpir.ump.edu.my/id/eprint/42513/
https://doi.org/10.54905/disssi.v21i55.e9ije1684
https://doi.org/10.54905/disssi.v21i55.e9ije1684
_version_ 1822924654239547392
score 13.235362

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