Li Yupeng, Zhao Dong, Liu Guangjie, Liu Yi, Bano Yasmeen, Ibrohimov Alisherjon, Chen Huiling, Wu Chengwen, Chen Xumin
College of Computer Science and Technology, Changchun Normal University, Changchun, China.
Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Front Neuroinform. 2022 Oct 31;16:956423. doi: 10.3389/fninf.2022.956423. eCollection 2022.
Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.
透析中低血压（IDH）是血液透析（HD）过程中发生的一种不良事件，发病率和死亡率较高。预防IDH的关键在于预测透析前情况并给予适当的超滤处方。为此，本文构建了一个预测模型（bCOWOA-KELM），利用血常规指标来预测IDH。在研究中，将正交学习机制应用于鲸鱼优化算法（WOA）的前半部分以提高搜索速度和准确性，将协方差矩阵应用于WOA的后半部分以增强跳出局部最优的能力和收敛精度。结合上述两种改进方法，本文首次提出了一种新颖的改进变体（COWOA）。此外，bCOWOA-KELM的核心是利用二进制COWOA来提高核极限学习机（KELM）的性能。为了验证该研究的综合性能，本文基于30个基准函数对COWOA设置了四类对比实验，并基于六个公共数据集和HD数据集对bCOWOA-KELM进行了一系列预测实验。最后，本文分别对实验结果进行了分析。对比实验结果充分证明COWOA优于其他著名方法。更重要的是，bCOWOA在特征选择方面表现优于同类方法，其准确率为92.41%。此外，bCOWOA比排名第二的bSCA准确率提高了0.32%，比排名最差的bGWO提高了3.63%。因此，所提出的模型可用于未来的IDH预测应用。
