Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

Xiaoyi Guo, Wei Zhou, Qun Lu, Aiyan Du, Yinghua Cai, Yijie Ding, Xiaoyi Guo, Wei Zhou, Qun Lu, Aiyan Du, Yinghua Cai, Yijie Ding

Abstract

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient's dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Copyright © 2021 Xiaoyi Guo et al.

Figures

Figure 1
Figure 1
Schematic of our proposed method.
Figure 2
Figure 2
The RMSE under different numbers of hidden layer nodes (SLapRVFL network).
Figure 3
Figure 3
The RMSE of iterations on the training set.
Figure 4
Figure 4
The RMSE under different λ1 and λ2.
Figure 5
Figure 5
Folded empirical cumulative distribution plot between different methods.
Figure 6
Figure 6
Bland–Altman plot analysis.
Algorithm 1
Algorithm 1
Algorithm 1. Algorithm of SLapRVFL

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