A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis

  • Claúdia Brito
  • Marisa EstevesEmail author
  • Hugo Peixoto
  • António Abelha
  • José Machado


Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients’ health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.


Data mining Knowledge extraction Chronic kidney diseases Continuous ambulatory peritoneal dialysis Serum creatinine Clinical decision support systems Weka Classification algorithms 



This work has been supported by Compete POCI-01-0145—FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Claúdia Brito
    • 1
  • Marisa Esteves
    • 2
    Email author
  • Hugo Peixoto
    • 2
  • António Abelha
    • 2
  • José Machado
    • 2
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.Algoritmi Research CenterUniversity of MinhoBragaPortugal

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