Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients

Erik Dovgan, Anton Gradišek, Mitja Luštrek, Mohy Uddin, Aldilas Achmad Nursetyo, Sashi Kiran Annavarajula, Yu Chuan Li, Shabbir Syed-Abdul

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient’s well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient’s first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such models could allow policy-makers better planning and allocation of resources for treatment. Using data from 8,492 patients, we obtained the area under the receiver operating characteristic curve (AUC) of 0.773 for predicting RRT within 12 months from the time of CKD diagnosis. The results also show that there is no additional advantage in focusing only on patients with diabetes in terms of prediction performance. Although these results are not as such suitable for adoption into clinical practice, the study provides a strong basis and a variety of approaches for future studies of forecasting models in healthcare.

Original languageEnglish
Article numbere0233976
JournalPLoS ONE
Issue number6
Publication statusPublished - Jun 2020

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General


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