Title:An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients
Volume: 16
Issue: 2
Author(s): Xiaoyi Guo, Wei Zhou, Bin Shi, Xiaohua Wang*, Aiyan Du*, Yijie Ding, Jijun Tang and Fei Guo*
Affiliation:
- Department of Urology, the First Affiliated Hospital of Soochow University, 215006, Suzhou,China
- Hemodialysis Center, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000, Wuxi,China
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, 300350, Tianjin,China
Keywords:
Dry weight, hemodialysis, machine learning, multiple kernel learning, support vector regression, blood pressure.
Abstract:
Background: Dry Weight (DW) is the lowest weight after dialysis, and patients with
lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches
have been presented to assess the dry weight of hemodialysis patients. However, these traditional
methods all depend on special instruments and professional technicians.
Objective: In order to avoid this limitation, we need to find a machine-independent way to assess dry
weight, therefore we collected some clinical influencing characteristic data and constructed a
Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients.
Methods: In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements,
and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body
Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were
closely related to their dry weight. All these relevant data were used to enter the regression equation.
Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS)
model was proposed to predict the dry weight of hemodialysis patients.
Results: The experimental results show that dry weight is positively correlated with BMI and HR.
And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively
correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was
1.3817.
Conclusion: Our proposed model could serve as a viable alternative for dry weight estimation of
hemodialysis patients, thus providing a new way for clinical practice.
