A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment

Abstract

The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy of minority class predictions. Therefore, we introduce a method called UnderXGBoost. This novel methodology combines the under-sampling, bagging, and XGBoost techniques to balance the dataset and improve predictive accuracy for the minority class. This method is characterized by its straightforward implementation and training efficiency. Empirical validation in a real-world dataset confirms the superior performance of UnderXGBoost compared to existing models in predicting intradialytic hypotension. Furthermore, our approach demonstrates versatility, allowing XGBoost to be substituted with other classifiers and still producing promising results. Sensitivity analysis was performed to assess the model's robustness, reinforce its reliability, and indicate its applicability to a broader range of medical scenarios facing similar challenges of data imbalance. Our model aims to enable medical professionals to provide preemptive treatments more effectively, thereby improving patient care and prognosis. This study contributes a novel and effective solution to a critical issue in medical prediction, thus broadening the application spectrum of predictive modeling in the healthcare domain.

Keywords: Class imbalance; Ensemble learning; Hemodialysis; Intradialytic hypotension; UnderXGBoost.