Open Access
| Issue |
EPJ Web Conf.
Volume 373, 2026
2nd International Conference on Sustainable Science and Technology for Tomorrow (SciTech-25)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 22 | |
| Section | Quantum Science, Computing and Intelligent Technologies | |
| DOI | https://doi.org/10.1051/epjconf/202637301002 | |
| Published online | 19 June 2026 | |
- World Health Organization, "Ήypertension, Fact Sheet, Geneva, Switzerland, Sep. 2025. [Online]. Available: doi: https://www.who.int/news-room/fact-sheets/d/tail/hypertension [Google Scholar]
- J. Du et al., "Developing a hypertension visualization risk prediction system utilizing machine learning and health check-up data,“ Scientific Reports, vol. 13, art. 18953, 2023. doi: 10.1038/s41598-023-46281-y [Google Scholar]
- E. Septian et al., Prediction of personalised hypertension using machine learning in Indonesian population, Journal of Medical Systems, vol. 49, art. 137, 2025. doi: 10.1007/S10916-025-02253-5 [Google Scholar]
- Y. Zhao, J. Liu, X. Li, Y. Yang, and L. Wang, Predicting the risk of hypertension based on several easy-to-collect risk factors: A machine learning method,“ Frontiers in Public Health, vol. 9, art. 619429,2021. doi: 10.3389/fpubh.2021.619429 [Google Scholar]
- Μ. Z. I. Chowdhury, Μ. S. Rahman, and H. Μ. Turin, Prediction ofhypertension using traditionairegression and machine leamingmodels: A systematic review and meta-analysis PLOS ONE, vol. 17, no. 4, e0266334,2022. doi: 10.1371/joumaLpone.0266334 [Google Scholar]
- R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need/" Information Fusion, vol. 81, pp. 84-90,2022. doi: 10.1016/j.inffus.2021.11.011 [Google Scholar]
- T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system/" in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016,pp. 785-794. doi: 10.1145/2939672.2939785 [Google Scholar]
- G. Ke et al, “LightGBM: A highly efficient gradient boosting decision tree,” in Advances in Neural Information Processing Systems 30 (NeurlPS 2017), pp. 3146-3154,2017. [Google Scholar]
- L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: Unbiased boosting with categorical features,"" in Advances in Neural Information Processing Systems 31 (NeurlPS 2018), pp. 6638-6648,2018. [Google Scholar]
- N. Hollmann et al., "Accurate predictions on small data with a tabular foundation modeV" Nature, vol. 627, pp. 769-776,2025. doi: 10.1038/s41586-025-08487-y [Google Scholar]
- H. Salmi, S. B. Yahia, and L. Rokach,"Handlmgimbalancedmedicaldatasets:Review of a decade of research, Artificial Intelligence Review, 2024. doi: 10.1007/s10462-024-10884-2 [Google Scholar]
- N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique/" Journal of Artificial Intelligence Research, vol. 16, pp. 321-357,2002. doi: 10.1613/jair.953 [CrossRef] [Google Scholar]
- D. Elreedy and A. F. Atiya, “A comprehensive analysis of synthetic minority oversampling technique (SMOTE) for handling class imbalance," Information Sciences, vol. 505, pp. 32-64,2019. doi: 10.1016/j.ins.2019.07.070 [CrossRef] [Google Scholar]
- T. Akiba, S. Sano, T. Yanase, T. Ohta, and Μ. Koyama, Optuna: A next-generation hyperparameter optimization framework," in Proc. 25th ACM SIGKDD Int. Conf Knowledge Discovery and Data Mining, 2019, pp. 2623-2631. doi: 10.1145/3292500.3330701 [Google Scholar]
- P. Lacerda, B. Barros, C. Albuquerque, and A. Conci, " Sensors, voL 21,no. 6, art. 2174, 2021. doi: 10.3390/S21062174 [Google Scholar]
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