Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.

BACKGROUND

Intradialytic hypotension (IDH) is commonly occurred and links to higher mortality among patients undergoing hemodialysis (HD). Its early prediction and prevention will dramatically improve the quality of life. However, predicting the occurrence of IDH clinically is not simple. The aims of this study are to develop an intelligent system with capability of predicting blood pressure (BP) during HD, and to further compare different machine learning algorithms for next systolic BP (SBP) prediction.

METHODS

This study presented comprehensive comparisons among linear regression model, least absolute shrinkage and selection operator (LASSO), tree-based ensemble machine learning models (random forest [RF] and extreme gradient boosting [XGBoost]), and support vector regression to predict the BP during HD treatment based on 200 and 48 maintenance HD patients containing a total of 7,180 and 2,065 BP records for the training and test dataset, respectively. Ensemble method also was computed to obtain better predictive performance. We compared the developed models based on R

RESULTS

We found that RF (R

CONCLUSIONS

We compared five machine learning and an ensemble method for next SBP prediction. Among all studied algorithms, the RF and the ensemble method have the better predictive performance. The prediction models using ensemble method for intradialytic BP profiling may be able to assist the HD staff or physicians in individualized care and prompt intervention for patients' safety and improve care of HD patients.

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare no conflicts of interest.