What type of study is this?

This is a Human Clinical Trial study.

September 10, 2025Open Access

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

ZRZulkifli RaisState University of Makassar MSM. S.Indian Agricultural Statistics Research Institute SSSaida SaidaState University of Makassar

Key Points

The xgboost model achieved an accuracy of 93.3%, indicating effective classification of hypertension levels.
Precision reached 97%, while recall was 92%, showcasing the balance between false positives and false negatives.
Hyperparameter tuning was performed using gridsearchcv to optimize the model performance during classification.
Good performance metrics imply that early detection of hypertension can be enhanced using machine learning techniques.

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.

AIに質問

Bookmark

View Full Paper