RT Journal Article
T1 Predicting mortality in hemodialysis patients using machine learning analysis
A1 Garcia-Montemayor, Victoria
A1 Martin-Malo, Alejandro
A1 Barbieri, Carlo
A1 Bellocchio, Francesco
A1 Soriano, Sagrario
A1 Pendon-Ruiz de Mier, Victoria
A1 Molina, Ignacio R.
A1 Aljama, Pedro
A1 Rodriguez, Mariano
K1 Haemodialysis
K1 Machine learning
K1 Mortality
K1 Predictive models
K1 Random forest
K1 Diálisis renal
K1 Aprendizaje automático
K1 Mortalidad
K1 Predicción
AB Background. Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients. Methods. Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session.Results. There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (DAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables. Conclusions. Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.
PB Oxford University Press on behalf of ERA-EDTA
SN 2048-8505
YR 2021
FD 2021
LK http://hdl.handle.net/10668/3778
UL http://hdl.handle.net/10668/3778
LA en
NO Garcia-Montemayor V, Martin-Malo A, Barbieri C, Bellocchio F, Soriano S, Pendon-Ruiz de Mier V, et al. Predicting mortality in hemodialysis patients using machine learning analysis. Clin Kidney J. 2020 Aug 11;14(5):1388-1395
DS RISalud
RD Apr 11, 2025