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How to cite this paper
APA: Hossain, F., Sumon, A & Hossain, M. (2026). Automated stress classification using machine learning: A feature engineering perspective. Healthcare Engineering, 2(3), 141-154.
Chicago/Turabian: Hossain, F., Sumon, A & Hossain, M. 2026. "Automated stress classification using machine learning: A feature engineering perspective." Healthcare Engineering 2, no. 3 (2026): 141-154.
AMA: Hossain, F., Sumon, A & Hossain, M. Automated stress classification using machine learning: A feature engineering perspective. Healthcare Engineering. 2026;2(3):141-154.
References
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Datar, D., & Khobragade, R. N. (2023). Mental state prediction using machine learning and EEG signal. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 07–12. https://doi.org/10.17762/ijritcc.v11i4.6374
Elzeiny, S., & Qaraqe, M. (2018). Machine learning approaches to automatic stress detection: A review. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA).
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018). Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing, 51, 1–26.
Gedam, S., & Paul, S. (2021). A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045–84066.
Ghosh, S., Kim, S., Ijaz, M. F., Singh, P. K., & Mahmud, M. (2022). Classification of mental stress from wearable physiological sensors using image-encoding-based deep neural network. Biosensors, 12(12), 1153.
Iqbal, T., Elahi, A., Wijns, W., & Shahzad, A. (2022). Exploring unsupervised machine learning classification methods for physiological stress detection. Frontiers in Medical Technology, 4.
Kene, A., & Thakare, S. (2022). Prediction of mental stress level based on machine learning. In Algorithms for Intelligent Systems (pp. 525–536).
Mohd Shafiee, N. S., & Mutalib, S. (2020). Prediction of mental health problems among higher education student using machine learning. International Journal of Education and Management Engineering, 10(6), 1–9.
Pabreja, K., Singh, A., Singh, R., Agnihotri, R., Kaushik, S., & Malhotra, T. (2020). Stress prediction model using machine learning. In Proceedings of International Conference on Artificial Intelligence and Applications (pp. 57–68).
Pankajavalli, P. B., Karthick, G. S., & Sakthivel, R. (2021). An efficient machine learning framework for stress prediction via sensor integrated keyboard data. IEEE Access, 9, 95023–95035.
Qiriro. (2019, January). Swell dataset. Kaggle. https://www.kaggle.com/datasets/qiriro/swell-heart-rate-variability-hrv
Rois, R., Ray, M., Rahman, A., & Roy, S. K. (2021). Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms. Journal of Health, Population and Nutrition, 40(1).
Shahapur, S. S., Chitti, P., Patil, S., Nerurkar, C. A., Shivannagol, V. S., Rayanaikar, V. C., Sawant, V., & Betageri, V. (2024). Decoding minds: Estimation of stress level in students using machine learning. Indian Journal of Science and Technology, 17(19), 2002–2012. https://doi.org/10.17485/ijst/v17i19.2951
Vos, G., Trinh, K., Sarnyai, Z., & Azghadi, M. R. (2023). Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices. Journal of Biomedical Informatics, 148, 104556.
Walambe, R., Nayak, P., Bhardwaj, A., & Kotecha, K. (2021). Employing multimodal machine learning for stress detection. Journal of Healthcare Engineering, 2021, 1–12.
Ahuja, R., & Banga, A. (2019). Mental stress detection in university students using machine learning algorithms. Procedia Computer Science, 152, 349–353.
Chung, J., & Teo, J. (2022). Mental health prediction using machine learning: Taxonomy, applications, and challenges. Applied Computational Intelligence and Soft Computing, 2022, 1–19.
Datar, D., & Khobragade, R. N. (2023). Mental state prediction using machine learning and EEG signal. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 07–12. https://doi.org/10.17762/ijritcc.v11i4.6374
Elzeiny, S., & Qaraqe, M. (2018). Machine learning approaches to automatic stress detection: A review. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA).
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018). Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing, 51, 1–26.
Gedam, S., & Paul, S. (2021). A review on mental stress detection using wearable sensors and machine learning techniques. IEEE Access, 9, 84045–84066.
Ghosh, S., Kim, S., Ijaz, M. F., Singh, P. K., & Mahmud, M. (2022). Classification of mental stress from wearable physiological sensors using image-encoding-based deep neural network. Biosensors, 12(12), 1153.
Iqbal, T., Elahi, A., Wijns, W., & Shahzad, A. (2022). Exploring unsupervised machine learning classification methods for physiological stress detection. Frontiers in Medical Technology, 4.
Kene, A., & Thakare, S. (2022). Prediction of mental stress level based on machine learning. In Algorithms for Intelligent Systems (pp. 525–536).
Mohd Shafiee, N. S., & Mutalib, S. (2020). Prediction of mental health problems among higher education student using machine learning. International Journal of Education and Management Engineering, 10(6), 1–9.
Pabreja, K., Singh, A., Singh, R., Agnihotri, R., Kaushik, S., & Malhotra, T. (2020). Stress prediction model using machine learning. In Proceedings of International Conference on Artificial Intelligence and Applications (pp. 57–68).
Pankajavalli, P. B., Karthick, G. S., & Sakthivel, R. (2021). An efficient machine learning framework for stress prediction via sensor integrated keyboard data. IEEE Access, 9, 95023–95035.
Qiriro. (2019, January). Swell dataset. Kaggle. https://www.kaggle.com/datasets/qiriro/swell-heart-rate-variability-hrv
Rois, R., Ray, M., Rahman, A., & Roy, S. K. (2021). Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms. Journal of Health, Population and Nutrition, 40(1).
Shahapur, S. S., Chitti, P., Patil, S., Nerurkar, C. A., Shivannagol, V. S., Rayanaikar, V. C., Sawant, V., & Betageri, V. (2024). Decoding minds: Estimation of stress level in students using machine learning. Indian Journal of Science and Technology, 17(19), 2002–2012. https://doi.org/10.17485/ijst/v17i19.2951
Vos, G., Trinh, K., Sarnyai, Z., & Azghadi, M. R. (2023). Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices. Journal of Biomedical Informatics, 148, 104556.
Walambe, R., Nayak, P., Bhardwaj, A., & Kotecha, K. (2021). Employing multimodal machine learning for stress detection. Journal of Healthcare Engineering, 2021, 1–12.