An Optimized Heterogeneous Stacking Ensemble with Hyperparameter Optimization for Multi Class Hypertension Risk Classification
DOI:
https://doi.org/10.59395/ijadis.v7i2.1637Keywords:
Hypertension Classification , Stacking Ensemble Learning , CatBoost , GridSearchCV , SHAPAbstract
Hypertension is a major global health concern and a leading risk factor for cardiovascular disease, stroke, kidney failure, and premature mortality. Accurate and interpretable prediction of hypertension risk is essential for supporting early intervention and preventive healthcare. This study proposes an optimized and explainable stacking ensemble framework for multi class hypertension classification by integrating LASSO feature selection, SMOTEENN data balancing, heterogeneous ensemble learning, CatBoost meta learning, GridSearchCV optimization, and SHAP based explainability. The proposed architecture combines five base learners, namely XGBoost, LightGBM, Random Forest, Extra Trees, and Support Vector Machine, whose probability outputs are transformed into meta features and processed by an optimized CatBoost meta learner. Experiments conducted on a dataset containing 3,000 hypertension related records demonstrated superior classification performance, achieving 97.50% accuracy, 97.46% precision, 97.50% recall, and 97.48% F1 score. 10 fold cross validation further confirmed the robustness of the framework with a mean accuracy of 97.50% ± 0.0027. ROC analysis produced AUC values above 0.97 for all classes, indicating excellent discriminative capability. The results demonstrate that the proposed framework provides both high predictive accuracy and strong interpretability, making it a promising solution for intelligent hypertension risk assessment and clinical decision support.
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[1] S. Goorani, S. Zangene, and J. D. Imig, “Hypertension: A Continuing Public Healthcare Issue,” 2025. doi: 10.3390/ijms26010123. DOI: https://doi.org/10.3390/ijms26010123
[2] G. Ambaw Kassie et al., “Undiagnosed hypertension and associated factors among adults in ethiopia: a systematic review and meta-analysis,” BMC Cardiovasc. Disord., vol. 23, no. 1, p. 278, 2023, doi: 10.1186/s12872-023-03300-0. DOI: https://doi.org/10.1186/s12872-023-03300-0
[3] M. Wójcik et al., “Psychosocial and environmental risk factors of obesity and hypertension in children and adolescents—a literature overview,” Front. Cardiovasc. Med., vol. Volume 10, 2023, doi: 10.3389/fcvm.2023.1268364. DOI: https://doi.org/10.3389/fcvm.2023.1268364
[4] J.-G. Wang, W. Zhang, Y. Li, and L. Liu, “Hypertension in China: epidemiology and treatment initiatives,” Nat. Rev. Cardiol., vol. 20, no. 8, pp. 531–545, 2023, doi: 10.1038/s41569-022-00829-z. DOI: https://doi.org/10.1038/s41569-022-00829-z
[5] I. Skalidis et al., “Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care,” 2025. doi: 10.3390/medicina61091597. DOI: https://doi.org/10.3390/medicina61091597
[6] H. Sadr et al., “Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches,” Eur. J. Med. Res., vol. 30, no. 1, p. 418, 2025, doi: 10.1186/s40001-025-02680-7. DOI: https://doi.org/10.1186/s40001-025-02680-7
[7] M. A. Ahmed, A. AbdelMoety, and A. M. A. Soliman, “Predicting cancer risk using machine learning on lifestyle and genetic data,” Sci. Rep., vol. 15, no. 1, p. 30458, 2025, doi: 10.1038/s41598-025-15656-8. DOI: https://doi.org/10.1038/s41598-025-15656-8
[8] N. Yanes, L. Jamel, B. Alabdullah, M. Ezz, A. M. Mostafa, and H. Shabana, “Using Machine Learning for Detection and Prediction of Chronic Diseases,” IEEE Access, vol. 12, pp. 177674–177691, 2024, doi: 10.1109/ACCESS.2024.3494839. DOI: https://doi.org/10.1109/ACCESS.2024.3494839
[9] A. ayub khan, R. ghodhbani, A. Alsufyani, N. Alsufyani, and M. A. Mohamed, “Leveraging blockchain-integrated explainable artificial intelligence (XAI) for ethical and personalized healthcare decision-making: a framework for secure data sharing and enhanced patient trust,” J. Supercomput., vol. 81, no. 15, p. 1353, 2025, doi: 10.1007/s11227-025-07844-0. DOI: https://doi.org/10.1007/s11227-025-07844-0
[10] M. Wang, “Explainable machine-learning-based cardiovascular disease prediction in patients with hypertension: Algorithm comparison and SHapley Additive exPlanations (SHAP) analysis,” Arch. Cardiovasc. Dis., vol. 119, no. 4, pp. 273–282, 2026, doi: https://doi.org/10.1016/j.acvd.2025.09.005. DOI: https://doi.org/10.1016/j.acvd.2025.09.005
[11] P. Mahajan, S. Uddin, F. Hajati, and M. A. Moni, “Ensemble Learning for Disease Prediction: A Review,” 2023. doi: 10.3390/healthcare11121808. DOI: https://doi.org/10.3390/healthcare11121808
[12] P. Sharma et al., “Revolutionizing Utility of Big Data Analytics in Personalized Cardiovascular Healthcare,” 2025. doi: 10.3390/bioengineering12050463. DOI: https://doi.org/10.3390/bioengineering12050463
[13] H. Kim, M. Khomidov, and J.-H. Lee, “XGBoost and SHAP-Based Analysis of Risk Factors for Hypertension Classification in Korean Postmenopausal Women,” 2025. doi: 10.3390/bioengineering12060659. DOI: https://doi.org/10.3390/bioengineering12060659
[14] A. W. Mirzaye, H. Saadatfar, and M. A. Nematollahi, “Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data,” Biomed Res. Int., vol. 2026, no. 1, p. 6335947, Jan. 2026, doi: https://doi.org/10.1155/bmri/6335947. DOI: https://doi.org/10.1155/bmri/6335947
[15] M. Salmi, D. Atif, D. Oliva, A. Abraham, and S. Ventura, “Handling imbalanced medical datasets: review of a decade of research,” Artif. Intell. Rev., vol. 57, no. 10, p. 273, 2024, doi: 10.1007/s10462-024-10884-2. DOI: https://doi.org/10.1007/s10462-024-10884-2
[16] K. Swanson, E. Wu, A. Zhang, A. A. Alizadeh, and J. Zou, “From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment,” Cell, vol. 186, no. 8, pp. 1772–1791, Apr. 2023, doi: 10.1016/j.cell.2023.01.035. DOI: https://doi.org/10.1016/j.cell.2023.01.035
[17] S. S. Rezk and K. S. Selim, “Metaheuristic-based ensemble learning: an extensive review of methods and applications,” Neural Comput. Appl., vol. 36, no. 29, pp. 17931–17959, 2024, doi: 10.1007/s00521-024-10203-4. DOI: https://doi.org/10.1007/s00521-024-10203-4
[18] R. Sovia, Y. Irawan, I. A. Wisky, M. H. Yuhandri, and R. Permana, “MLSEL: A tuned multilayer stacking ensemble learning with meta feature for flood risk prediction,” Eng. Appl. Sci. Res., vol. 53, no. 2 SE-ORIGINAL RESEARCH, pp. 211–222, Apr. 2026, doi: 10.64960/easr.2026.263071. DOI: https://doi.org/10.64960/easr.2026.263071
[19] Y. Irawan, Refni Wahyuni, and Herianto, “An Integrated Machine Learning and Deep Learning Approach for Multiclass Flood Risk Classification with Feature Selection and Imbalanced Data Handling,” J. Teknol. DAN OPEN SOURCE, vol. 8, no. 2 SE-Articles, pp. 650–661, Dec. 2025, doi: 10.36378/jtos.v8i2.4639. DOI: https://doi.org/10.36378/jtos.v8i2.4639
[20] Y. Irawan, S. Defit, and R. Sovia, “Optimizing Stacking Ensemble Learning to Enhance Meta-Model Performance in Forest Fire Risk Level Detection,” 2025 1st Int. Conf. Emerg. Trends Inf. Syst. Informatics, pp. 1–6, 2025, doi: 10.1109/ICETISI67983.2025.11406049. DOI: https://doi.org/10.1109/ICETISI67983.2025.11406049
[21] Y. Irawan, S. Defit, and R. Sovia, “Rotational Stacking Ensemble with Accuracy-Based Weighting for Real-Time Forest Fire Risk Prediction,” Int. J. Robot. Control Syst., vol. 6, no. 3, May 2026, doi: https://doi.org/10.31763/ijrcs.v6i3.2741.
[22] L. Liu, G. Zhao, W. Liang, and Z. Jian, “Hybrid stacking ensemble algorithm and simulated annealing optimization for stability evaluation of underground entry-type excavations,” Undergr. Sp., vol. 17, pp. 25–44, 2024, doi: 10.1016/j.undsp.2023.11.002. DOI: https://doi.org/10.1016/j.undsp.2023.11.002
[23] Y. Devis, Muhamadiah, Yulanda, Y. Irawan, and R. Wahyuni, “Optimization of Machine Learning Models for Risk Prediction of DHF Spread to Support Management Strategies in Urban Areas,” J. Appl. Data Sci., vol. 6, no. 4, pp. 2407–2420, 2025, doi: 10.47738/jads.v6i4.898. DOI: https://doi.org/10.47738/jads.v6i4.898
[24] A. Abdollahi and B. Pradhan, “Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model,” Sci. Total Environ., vol. 879, p. 163004, 2023, doi: https://doi.org/10.1016/j.scitotenv.2023.163004. DOI: https://doi.org/10.1016/j.scitotenv.2023.163004
[25] S. Reza, R. Amin, R. Yasmin, W. Kulsum, and S. Ruhi, “Heliyon Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data,” Heliyon, vol. 10, no. 2, p. e24536, 2024, doi: 10.1016/j.heliyon.2024.e24536. DOI: https://doi.org/10.1016/j.heliyon.2024.e24536
[26] Y. Li, G. Li, K. Wang, Z. Wang, and Y. Chen, “Forest Fire Risk Prediction Based on Stacking Ensemble Learning for Yunnan Province of China,” 2024. DOI: https://doi.org/10.3390/fire7010013
[27] 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. DOI: https://doi.org/10.1016/j.jafr.2024.101107
[28] S. Mondal, S. Ghosh, and A. Nag, “Brain stroke prediction model based on boosting and stacking ensemble approach,” Int. J. Inf. Technol., vol. 16, no. 1, pp. 437–446, 2024, doi: 10.1007/s41870-023-01418-0. DOI: https://doi.org/10.1007/s41870-023-01418-0
[29] 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. DOI: https://doi.org/10.33166/AETiC.2023.01.003
[30] L. R. Kalabarige, R. S. Rao, A. R. Pais, and L. A. Gabralla, “A Boosting-Based Hybrid Feature Selection and Multi-Layer Stacked Ensemble Learning Model to Detect Phishing Websites,” IEEE Access, vol. 11, no. July, pp. 71180–71193, 2023, doi: 10.1109/ACCESS.2023.3293649. DOI: https://doi.org/10.1109/ACCESS.2023.3293649
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