Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification

Zulkifli Rais; Muhammad Fahmuddin S; Saida Saida; Agung Triutomo

doi:10.35877/454RI.asci4191

Authors

Zulkifli Rais Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia
Muhammad Fahmuddin S Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia
Saida Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia
Agung Triutomo Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia

DOI:

https://doi.org/10.35877/454RI.asci4191

Keywords:

Hypertension, Machine Learning, XGBoost

Abstract

The increasing number of hypertension patients and the threat of serious complications make hypertension one of the leading causes of death worldwide. Early prevention is currently considered one of the best solutions. Early prevention through early detection can be achieved by utilizing machine learning technology. XGBoost is a machine learning algorithm based on gradient boosting machines. XGBoost applies regularization techniques to reduce overfitting and has faster execution speed as well as better performance. The objective of this research is to classify hypertension levels using the XGBoost method and leveraging hyperparameter tuning for optimization. In this study, the hyperparameter optimization technique used is gridsearchCV. The evaluation results of the XGBoost classification method using the best combination of parameters show good performance, where the XGBoost model achieves an accuracy of 93.3%, Precision of 97%, Recall of 92%, F1-Score of 93%, and AUC value of 0.935. This implies that the classification of hypertension levels in patients at Pelamonia Makassar Hospital can be well or accurately classified using the XGBoost method.

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References

Ahmar, A. S., Meliyana, S. M., Botto-Tobar, M., & Hidayat, R. (2024). The Comparison of Single and Double Exponential Smoothing Models in Predicting Passenger Car Registrations in Canada. Daengku: Journal of Humanities and Social Sciences Innovation, 4(2), 367-371. https://doi.org/10.35877/454RI.daengku2639

Chang, W., Liu, Y., Xiao, Y., Yuan, X., Xu, X., & Zhang, S. (2019). A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data.

Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2939672.2939785

Friedman, J. H. (2001a). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232.

Friedman, J., Tibshirani, R., & Hastie, T. (2000). Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). The Annals of Statistics, 28(2), 337–407. https://doi.org/10.1214/aos/1016120463

Gorunescu, F. (2011a). Data Mining Concepts, Models and Techniques. Springer International Publishing.

Gorunescu, F. (2011b). Data Mining Concepts, Models and Techniques. Springer International Publishing.

Goyal, J., Khandnor, P., & Aseri, T. C. (2021). A Comparative Analysis of Machine Learning classifiers for Dysphonia-based classification of Parkinson’s Disease. International Journal of Data Science and Analytics, 11(1), 69–83. https://doi.org/10.1007/s41060-020-00234-0

Hanif, I. (2020). Implementing Extreme Gradient Boosting (XGBoost) Classifier to Improve Customer Churn Prediction. Proceedings of EAI International Conference. https://doi.org/10.4108/eai.2-8-2019.2290338

Jamerson, K. A., Jones, D. W., MacLaughlin, E. J., Muntner, P., Ovbiagele, B., Smith, S. C., Spencer, C. C., Stafford, R. S., Taler, S. J., Thomas, R. J., Williams, K. A., Williamson, J. D., & Wright, J. T. (2017). 2017 Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. In American College of Cardiology. https://doi.org/10.1016/j.jacc.2017.07.745

Katoch, S., Singh, V., & Tiwary, U. S. (2022). Indian Sign Language recognition system using SURF with SVM and CNN. Array, 14, 100141. https://doi.org/10.1016/j.array.2022.100141

Koshimizu, H., Kojima, R., & Okuno, Y. (2020). Future possibilities for artificial intelligence in the practical management of hypertension. Hypertension Research, 43(12), 1327–1337. https://doi.org/10.1038/s41440-020-0498-x

Lenny, L., Violyn, V., Ridwan, A., & Yennimar, Y. (2025). Comparison of C4. 5 & Random Forest Based on AdaBoost For Determining Loan Eligibility Customer Funds. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 9(1), 455–461.

Liew, X. Y., Hameed, N., & Clos, J. (2021). An investigation of XGBoost-based algorithm for breast cancer classification. Machine Learning with Applications, 6, 100154. https://doi.org/https://doi.org/10.1016/j.mlwa.2021.100154

Lindblom, S., Ivarsson, C., Wändell, P., Bergqvist, M., Norrman, A., Eriksson, J., Lund, L., Hagströmer, M., Hasselström, J., Sandlund, C., & others. (2024). Lifestyle counseling in patients with hypertension in primary health care and its association with antihypertensive pharmacotherapy. The Journal of Clinical Hypertension, 26(7), 816–824.

Liu, X., & Wang, S. (2024). Credit Card Default Prediction Based on XGBoost. Proceedings of the 2024 International Conference on Machine Intelligence and Digital Applications, 374–382.

López-Martínez, F., Schwarcz, A., Núñez-Valdez, E. R., & García-Díaz, V. (2018). Machine learning classification analysis for a hypertensive population as a function of several risk factors. Expert Systems with Applications, 110, 206–215. https://doi.org/10.1016/j.eswa.2018.06.006

Luo, Y., Li, Y., Lu, Y., Lin, S., & Liu, X. (2018). The prediction of hypertension based on convolution neural network. 2018 IEEE 4th International Conference on Computer and Communications (ICCC), 2122–2127. https://doi.org/10.1109/CompComm.2018.8780834

Luque, A., Carrasco, A., Martín, A., & de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231. https://doi.org/10.1016/j.patcog.2019.02.023

Maalik, Si., Ananta, K. W., & Wahjuni, S. (2019). Comparation Analysis of Ensemble Technique With Boosting (Xgboost) and Bagging (Randomforest) for Classify Splice Junction Dna Sequence Category. Jurnal Penelitian Pos Dan Informatika, 9(1), 27–36. https://doi.org/10.17933/jppi.2019.090

Martinez-Ríos, E., Montesinos, L., Alfaro-Ponce, M., & Pecchia, L. (2021). A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomedical Signal Processing and Control, 68, 102813. https://doi.org/10.1016/j.bspc.2021.102813

Mo, H., Sun, H., Liu, J., & Wei, S. (2019). Developing window behavior models for residential buildings using XGBoost algorithm. Energy and Buildings, 205, 109564. https://doi.org/10.1016/j.enbuild.2019.109564

Mroz, T., Griffin, M., Cartabuke, R., Laffin, L., Russo-Alvarez, G., Thomas, G., Smedira, N., Meese, T., Shost, M., & Habboub, G. (2024). Predicting hypertension control using machine learning. Plos One, 19(3), e0299932.

Nour, M., & Polat, K. (2020). Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms. Mathematical Problems in Engineering, 2020, 1–14. https://doi.org/10.1155/2020/2742781

Pavan, R., Nara, M., Gopinath, S., & Patil, N. (2021). Bayesian optimization and gradient boosting to detect phishing websites. 2021 55th Annual Conference on Information Sciences and Systems (CISS), 2–6. https://doi.org/10.1109/CISS50987.2021.9400317

Putatunda, S., & Rama, K. (2018). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. ACM International Conference Proceeding Series, 6–10. https://doi.org/10.1145/3297067.3297080

Shouri, S. (2024). Validation of health insurance customers using XGBoost modeling. Journal of International Crisis and Risk Communication Research, 7(S12), 1016.

Sianipar, E. A. (2025). Optimization of Diabetes Disease Classification Using Learning Vector Quantization Algorithm (LVQ). Journal of Computer Science and Informatics Engineering, 4(2), 72–84.

Subasi Abdulhamit and Yaman, E. (2020). EMG Signal Classification Using Discrete Wavelet Transform and Rotation Forest. In R. and G. P. L. Badnjevic Almir and Škrbić (Ed.), CMBEBIH 2019 (pp. 29–35). Springer International Publishing.

Sumathi, K., Kannan, S., & Nagarajan, K. (2016). Data Mining: Analysis of student database using Classification Techniques. International Journal of Computer Applications, 141(8), 22–27. https://doi.org/10.5120/ijca2016909703

Syarif, I., Zaluska, E., Prugel-Bennett, A., & Wills, G. (2012). Application of bagging, boosting and stacking to intrusion detection. International Workshop on Machine Learning and Data Mining in Pattern Recognition, 593–602.

Tsai, C.-F., Sue, K.-L., Hu, Y.-H., & Chiu, A. (2021). Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction. Journal of Business Research, 130, 200–209. https://doi.org/10.1016/j.jbusres.2021.03.018

Vemulapalli, L., Devendra, K. S., Satwik, G. S. S., & Kiran, T. U. (2024). Integrating SMOTE and XGBoost for enhanced Hepatitis Detection. 2024 Asia Pacific Conference on Innovation in Technology (APCIT), 1–6.

Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26–40. https://doi.org/10.11989/JEST.1674-862X.80904120

Xiong, X., Hu, S., Sun, D., Hao, S., Li, H., & Lin, G. (2022). Detection of false data injection attack in power information physical system based on SVM–GAB algorithm. Energy Reports, 8, 1156–1164. https://doi.org/10.1016/j.egyr.2022.02.290

Zhang, L., & Zhan, C. (2017). Machine Learning in Rock Facies Classification: An Application of XGBoost. International Geophysical Conference (IGC), 1371–1374. https://doi.org/10.1190/igc2017-351

Zhang, Y., Miao, D., Wang, J., & Zhang, Z. (2019). A cost-sensitive three-way combination technique for ensemble learning in sentiment classification. International Journal of Approximate Reasoning, 105, 85–97. https://doi.org/10.1016/j.ijar.2018.10.019