Open AccessPredicting mortality in hemodialysis patients using machine learning analysis
Author(s) -
V. García-Montemayor,
Alejandro MartínMalo,
C. Barbieri,
Francesco Bellocchio,
Sagrario Soriano,
Victoria Pendon-Ruiz De Mier,
Ignacio R Molina,
Pedro Aljama,
Mariano Rodríguez
Publication year - 2020
Publication title -
clinical kidney journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.033
H-Index - 40
eISSN - 2048-8513
pISSN - 2048-8505
DOI - 10.1093/ckj/sfaa126
Subject(s) - logistic regression , random forest , medicine , predictive modelling , statistics , hemodialysis , regression analysis , factor regression model , machine learning , emergency medicine , computer science , mathematics , bayesian multivariate linear regression , proper linear model
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 (ΔAUC 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.