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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Discovery Scientific Society
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42513/ http://umpir.ump.edu.my/id/eprint/42513/1/e9ije1684.pdf |
| _version_ | 1848826631080116224 |
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| author | Arya, Suraj Anju, . Nor Azuana, Ramli |
| author_facet | Arya, Suraj Anju, . Nor Azuana, Ramli |
| author_sort | Arya, Suraj |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:47:53Z |
| format | Article |
| id | ump-42513 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:47:53Z |
| publishDate | 2024 |
| publisher | Discovery Scientific Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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) |
| title | 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_short | 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) |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/42513/ http://umpir.ump.edu.my/id/eprint/42513/ http://umpir.ump.edu.my/id/eprint/42513/ http://umpir.ump.edu.my/id/eprint/42513/1/e9ije1684.pdf |