.png)
A Comprehensive Stacking Ensemble Approach for Stress Level Classification in Higher Education
Abstract
This research focuses on developing a comprehensive ensemble stacking model for the classification of student stress levels in higher education environments, specifically at Hang Tuah University Pekanbaru. Using a physiological dataset that includes parameters such as SPO2, heart rate, body temperature, systolic, and diastolic pressure, this research categorizes the condition of college students into four main categories: anxious, calm, tense, and relaxed. The data taken from public health centers in the period 2021 to 2024 was processed using the SMOTE technique to overcome data imbalance and K-Fold Cross Validation for model validation. In model development, a combination of basic algorithms such as SVM, Logistic Regression, Multilayer Perceptron, and Random Forest is used which is enhanced by boosting techniques through ADABoost, and XGBoost as a meta model. The test results show that the proposed stacking model is able to achieve 95% accuracy, with an AUC of 0.95, which indicates excellent performance in classification. The model not only excels in detecting more extreme stress conditions such as anxiety, but also shows reliable ability in classifying more difficult to distinguish conditions such as tense and relaxed. The conclusion of this study shows that the applied stacking ensemble approach significantly improves prediction accuracy and stability compared to traditional models. For future research, it is recommended to explore the use of deep learning-based meta-models such as LSTM and BiLSTM as well as rotation techniques in stacking to improve model performance and flexibility. The findings are expected to contribute significantly to the development of more sophisticated and effective stress detection models.
Keywords
Full Text:
PDFReferences
A. Nemcova et al., “Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone,” Biomed. Signal Process. Control, vol. 59, no. August, pp. 1–10, 2020, doi: 10.1016/j.bspc.2020.101928.
Y. Irawan, Y. Fernando, and R. Wahyuni, “Detecting Heart Rate Using Pulse Sensor As Alternative Knowing Heart Condition,” J. Appl. Eng. Technol. Sci., vol. 1, no. 1, pp. 30–42, 2019, doi: 10.37385/jaets.v1i1.16.
J. Gerald Dcruz and P. Yeh, “The Accuracy of Pulse Oxygen Saturation, Heart Rate, Blood Pressure, and Respiratory Rate Raised by a Contactless Telehealth Portal: Validation Study,” JMIR Form. Res., vol. 8, no. June, pp. 1–9, 2024, doi: 10.2196/55361.
Herianto, B. Kurniawan, Z. H. Hartomi, Y. Irawan, and M. K. Anam, “Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction,” J. Appl. Data Sci., vol. 5, no. 3, pp. 1272–1285, 2024.
T. Velmurugan and J. Dhinakaran, “A Novel Ensemble Stacking Learning Algorithm for Parkinson’s Disease Prediction,” Math. Probl. Eng., vol. 2022, 2022, doi: 10.1155/2022/9209656.
H. Zhang, H. A. Loaiciga, and T. Sauter, “A Novel Fusion-Based Methodology for Drought Forecasting,” Remote Sens., vol. 16, no. 5, 2024, doi: 10.3390/rs16050828.
A. Ghasemieh, A. Lloyed, P. Bahrami, P. Vajar, and R. Kashef, “A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients,” Decis. Anal. J., vol. 7, no. May, p. 100242, 2023, doi: 10.1016/j.dajour.2023.100242.
Z. Liao, M. Su, G. Ning, Y. Liu, T. Wang, and J. Zhou, “A Novel Stacked Generalization Ensemble-Based Hybrid PSVM-PMLP-MLR Model for Energy Consumption Prediction of Copper Foil Electrolytic Preparation,” IEEE Access, vol. 9, pp. 5821–5831, 2021, doi: 10.1109/ACCESS.2020.3048714.
M. Gollapalli et al., “A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM,” Comput. Biol. Med., vol. 147, no. June, 2022, doi: 10.1016/j.compbiomed.2022.105757.
S. Lin, X. Nong, J. Luo, and C. Wang, “A Novel Multi-Model Stacking Ensemble Learning Method for Metro Traction Energy Prediction,” IEEE Access, vol. 10, no. November, pp. 129231–129244, 2022, doi: 10.1109/ACCESS.2022.3228441.
S. K. Kalagotla, S. V. Gangashetty, and K. Giridhar, “A novel stacking technique for prediction of diabetes,” Comput. Biol. Med., vol. 135, no. February, p. 104554, 2021, doi: 10.1016/j.compbiomed.2021.104554.
H. Zheng, S. W. A. Sherazi, and J. Y. Lee, “A Stacking Ensemble Prediction Model for the Occurrences of Major Adverse Cardiovascular Events in Patients with Acute Coronary Syndrome on Imbalanced Data,” IEEE Access, vol. 9, pp. 113692–113704, 2021, doi: 10.1109/ACCESS.2021.3099795.
C. Li, B. Xu, Z. Chen, X. Huang, J. He, and X. Xie, “A Stacking Model-Based Classification Algorithm Is Used to Predict Social Phobia,” Appl. Sci., vol. 14, no. 1, 2024, doi: 10.3390/app14010433.
R. Forest, A. Boosting, and G. N. Bayes, “RAGN-L : A stacked ensemble learning technique for classification of Fire-Resistant columns,” vol. 240, no. September 2022, 2024, doi: 10.1016/j.eswa.2023.122491.
M. A. Rahim, M. A. Hossain, M. N. Hossain, J. Shin, and K. S. Yun, “Stacked Ensemble-Based Type-2 Diabetes Prediction Using Machine Learning Techniques,” Ann. Emerg. Technol. Comput., vol. 7, no. 1, pp. 30–39, 2023, doi: 10.33166/AETiC.2023.01.003.
H. Karamti, R. Alharthi, A. Al Anizi, R. M. Alhebshi, and A. A. Eshmawi, “Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach,” Cancer, vol. 15, no. 17, pp. 1–19, 2023, doi: https://doi.org/10.3390/cancers15174412.
A. J. Mohammed, “Improving Classification Performance for a Novel Imbalanced Medical Dataset using SMOTE Method,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3161–3172, 2020, doi: 10.30534/ijatcse/2020/104932020.
S. Wang, Y. Dai, J. Shen, and J. Xuan, “Research on expansion and classification of imbalanced data based on SMOTE algorithm,” Sci. Rep., vol. 11, no. 1, pp. 1–11, 2021, doi: 10.1038/s41598-021-03430-5.
D. A. Kristiyanti, S. A. Sanjaya, V. C. Tjokro, and J. Suhali, “Dealing imbalance dataset problem in sentiment analysis of recession in Indonesia,” IAES Int. J. Artif. Intell., vol. 13, no. 2, pp. 2058–2070, 2024, doi: 10.11591/ijai.v13.i2.pp2060-2072.
A. J. Barid, Hadiyanto, and A. Wibowo, “Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification,” Indones. J. Electr. Eng. Comput. Sci., vol. 33, no. 3, pp. 1632–1640, 2024, doi: 10.11591/ijeecs.v33.i3.pp1632-1640.
T. R. Mahesh, V. K. V, D. K. V, O. Geman, and M. Margala, “Healthcare Analytics The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification,” Healthc. Anal., vol. 4, no. July, pp. 1–10, 2023.
C. Chen et al., “Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier,” Comput. Biol. Med., vol. 123, no. June, p. 103899, 2020, doi: 10.1016/j.compbiomed.2020.103899.
M. Barton and B. Lennox, “Model stacking to improve prediction and variable importance robustness for soft sensor development,” Digit. Chem. Eng., vol. 3, no. May, p. 100034, 2022, doi: 10.1016/j.dche.2022.100034.
S. Rezaei Melal, M. Aminian, and S. M. Shekarian, “A machine learning method based on stacking heterogeneous ensemble learning for prediction of indoor humidity of greenhouse,” J. Agric. Food Res., vol. 16, p. 101107, 2024, doi: 10.1016/j.jafr.2024.101107.
S. Nyaramneni, “ScienceDirect Advanced Ensemble Machine Learning Models to Predict SDN Advanced Ensemble Machine Learning Models to Predict SDN Tra ffi c Tra ffi,” Procedia Comput. Sci., vol. 230, pp. 417–426, 2024, [Online]. Available: https://doi.org/10.1016/j.procs.2023.12.097
A. Almohimeed et al., “Explainable Artificial Intelligence of Multi-Level Stacking Ensemble for Detection of Alzheimer’s Disease Based on Particle Swarm Optimization and the Sub-Scores of Cognitive Biomarkers,” IEEE Access, vol. 11, no. November, pp. 123173–123193, 2023, doi: 10.1109/ACCESS.2023.3328331.
L. Qian-Chuan, X. Shi-Wei, Z. Jia-Yu, L. Jia-Jia, Z. Yi, and Z. Ze-Xi, “Ensemble learning prediction of soybean yields in China based on meteorological data,” Sci. Agric. Sin., vol. 22, no. 6, pp. 1909–1927, 2023, doi: 10.1016/j.jia.2023.02.011.
S. S. Kshatri, D. Singh, B. Narain, S. Bhatia, M. T. Quasim, and G. R. Sinha, “An Empirical Analysis of Machine Learning Algorithms for Crime Prediction Using Stacked Generalization: An Ensemble Approach,” IEEE Access, vol. 9, pp. 67488–67500, 2021, doi: 10.1109/ACCESS.2021.3075140.
H. R. Seireg, Y. M. K. Omar, F. E. A. El-Samie, A. S. El-Fishawy, and A. Elmahalawy, “Ensemble Machine Learning Techniques Using Computer Simulation Data for Wild Blueberry Yield Prediction,” IEEE Access, vol. 10, pp. 64671–64687, 2022, doi: 10.1109/ACCESS.2022.3181970.
M. K. Anam, S. Defit, Haviluddin, L. Efrizoni, and M. B. Firdaus, “Early Stopping on CNN-LSTM Development to Improve Classification Performance,” J. Appl. Data Sci., vol. 5, no. 3, pp. 1175–1188, 2024, doi: 10.47738/jads.v5i3.312.
W. Bourequat and H. Mourad, “Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine,” Int. J. Adv. Data Inf. Syst., vol. 2, no. 1, pp. 36–44, 2021, doi: 10.25008/ijadis.v2i1.1216.
S. Mondal, R. Maity, Y. Omo, S. Ghosh, and A. Nag, “An Efficient Computational Risk Prediction Model of Heart Diseases Based on Dual-Stage Stacked Machine Learning Approaches,” IEEE Access, vol. 12, no. January, pp. 7255–7270, 2024, doi: 10.1109/ACCESS.2024.3350996.
A. Febriani, R. Wahyuni, Y. Irawan, and R. Melyanti, “Improved Hybrid Machine and Deep Learning Model for Optimization of Smart Egg Incubator,” J. Appl. Data Sci., vol. 5, no. 3, pp. 1052–1068, 2024.
DOI: https://doi.org/10.47738/jads.v5i4.388
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0