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.

中文翻译：

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一种提高血液透析治疗期间透析中低血压预测准确性的装袋方法

本研究的主要目的是提高血液透析患者透析中低血压的预测准确性。在这种情况下，一个重大挑战来自于从监控设备导出的数据的性质，并表现出极端的类别不平衡问题。传统的预测模型通常表现出对多数类别的偏见，从而损害了少数类别预测的准确性。因此，我们引入了一种称为 UnderXGBoost 的方法。这种新颖的方法结合了欠采样、装袋和 XGBoost 技术来平衡数据集并提高少数类别的预测准确性。该方法的特点是实现简单、训练效率高。现实数据集中的经验验证证实了 UnderXGBoost 与现有模型相比在预测透析中低血压方面具有优越的性能。此外，我们的方法展示了多功能性，允许 XGBoost 被其他分类器替代，并且仍然产生有希望的结果。进行敏感性分析是为了评估模型的稳健性，增强其可靠性，并表明其适用于面临类似数据不平衡挑战的更广泛的医疗场景。我们的模型旨在使医疗专业人员能够更有效地提供先发性治疗，从而改善患者护理和预后。这项研究为医学预测中的关键问题提供了一种新颖有效的解决方案，从而拓宽了预测模型在医疗保健领域的应用范围。