Navigating the Depths of Intradialytic Hypotension Prediction

Published on October 31, 2022

In the vast ocean of hemodialysis, intradialytic hypotension (IDH) is a turbulent wave that can cause serious harm. But fear not! A team of clever researchers have devised a unique and powerful tool called bCOWOA-KELM to predict IDH. Imagine this tool as a skilled captain steering their ship through treacherous waters. To ensure accurate predictions, they combine the speed and precision of orthogonal learning with the versatility of the covariance matrix. This creates a new variant, COWOA, which outperforms other methods in predicting IDH. The experiments confirm its superiority, showing us that it’s like a well-trained dolphin leaping effortlessly ahead of the pack. Its feature selection is impeccable, boasting an impressive accuracy of 92.41%. This surpasses its closest competitor by 0.32% and outshines the worst-ranking method by a staggering 3.63%. So hop on board this exciting research and explore the depths of IDH prediction with bCOWOA-KELM!

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.

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