Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Korea.
Department of Big Data Engineering, Soonchunhyang University, Asan-si 31538, Korea.
Sensors (Basel). 2021 Jan 14;21(2):544. doi: 10.3390/s21020544.
End stage renal disease (ESRD) is the last stage of chronic kidney disease that requires dialysis or a kidney transplant to survive. Many studies reported a higher risk of mortality in ESRD patients compared with patients without ESRD. In this paper, we develop a model to predict postoperative complications, major cardiac event, for patients who underwent any type of surgery. We compare several widely-used machine learning models through experiments with our collected data yellow of size 3220, and achieved F1 score of 0.797 with the random forest model. Based on experimental results, we found that features related to operation (e.g., anesthesia time, operation time, crystal, and colloid) have the biggest impact on model performance, and also found the best combination of features. We believe that this study will allow physicians to provide more appropriate therapy to the ESRD patients by providing information on potential postoperative complications.
终末期肾病（ESRD）是慢性肾脏病的最后阶段，需要透析或肾移植才能存活。许多研究报告称，与没有 ESRD 的患者相比，ESRD 患者的死亡率更高。在本文中，我们开发了一种模型来预测接受任何类型手术的患者的术后并发症和主要心脏事件。我们通过使用我们收集的 3220 个大小的数据集进行实验，比较了几种广泛使用的机器学习模型，并使用随机森林模型获得了 0.797 的 F1 分数。根据实验结果，我们发现与手术相关的特征（例如，麻醉时间、手术时间、晶体和胶体）对模型性能的影响最大，并且还找到了最佳的特征组合。我们相信，这项研究将通过提供潜在术后并发症的信息，使医生能够为 ESRD 患者提供更合适的治疗。
