2022
Brito, C.; Esteves, M.; Peixoto, H.; Abelha, A.; Machado, J.
A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis Journal Article
In: Wireless Networks, vol. 28, no. 3, pp. 1269-1277, 2022, ISSN: 10220038, (cited By 6).
Abstract | Links | BibTeX | Etiquetas: Chronic kidney disease; Classification algorithm; Clinical decision support systems; Knowledge extraction; Peritoneal dialysis; Serum creatinine; Weka, Data mining, Decision making; Decision support systems; Dialysis; Patient treatment
@article{Brito20221269,
title = {A data mining approach to classify serum creatinine values in patients undergoing continuous ambulatory peritoneal dialysis},
author = {C. Brito and M. Esteves and H. Peixoto and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060728770&doi=10.1007%2fs11276-018-01905-4&partnerID=40&md5=9a8c346b171c728d75182e271555b144},
doi = {10.1007/s11276-018-01905-4},
issn = {10220038},
year = {2022},
date = {2022-01-01},
journal = {Wireless Networks},
volume = {28},
number = {3},
pages = {1269-1277},
publisher = {Springer},
abstract = {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. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.},
note = {cited By 6},
keywords = {Chronic kidney disease; Classification algorithm; Clinical decision support systems; Knowledge extraction; Peritoneal dialysis; Serum creatinine; Weka, Data mining, Decision making; Decision support systems; Dialysis; Patient treatment},
pubstate = {published},
tppubtype = {article}
}
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. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.