Journal of Applied Science, Engineering, Technology, and Education
Vol. 7 No. 1 (2025), Pages 126-136 · 2025-04-30
Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification
- Zulkifli Rais — Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia, Indonesia
- Muhammad Fahmuddin S — Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia, Indonesia
- Saida — Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia, Indonesia
- Agung Triutomo — Department of Statistics, Universitas Negeri Makassar, Makassar, Indonesia, Indonesia
- Publication History
- Published online: April 30, 2025
- DOI
- https://doi.org/10.35877/454RI.asci4191
- Copyright
- Copyright (c) 2025 Zulkifli Rais, Muhammad Fahmuddin S, Saida, Agung Triutomo (Author)
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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How to Cite
Rais, Z., Fahmuddin S, M., Saida, S., & Triutomo, A. (2025). Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification. Journal of Applied Science, Engineering, Technology, and Education, 7(1), 126–136. https://doi.org/10.35877/454RI.asci4191
