Hemodialysis Patient Death Prediction Using Logistic Regression

Authors

  • Dony Novaliendry Universitas Negeri Padang, Padang, Indonesia; National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan https://orcid.org/0000-0003-2653-0467
  • Oktoria
  • Cheng-Hong Yang National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
  • Yenny Desnelita Institute Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia
  • Irwan Institute Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia
  • Roni Sanjaya Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia
  • Gustientiedina Institute Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia
  • Yaslinda Lizar Universitas Islam Negeri Imam Bonjol, Padang, Indonesia
  • Noper Ardi

DOI:

https://doi.org/10.3991/ijoe.v19i09.40917

Keywords:

Logistic Regression, Diabetes, Hemodialysis, Prediction

Abstract


Hemodialysis is a procedure for cleaning the blood from the waste products of the body’s metabolism. this is one of modality to treat end stage kidney disease. There are two main classifications of this disease, namely acute kidney failure and chronic kidney failure. Kidney failure occurs when kidney damage is severe enough or lasts a long time so that the disease is generally the final stage of kidney disease. Dialysis is performed on patients with kidney failure, both acute kidney failure and chronic kidney failure. This study is aimed to predict the mortality risk of hemodialysis patients. The Taiwanese hemodialysis center enrolled a total of 665 hemodialysis patients. The prediction is based on Logistic Regression. Compared with K-Nearest Neighbor, linear discriminant, Tree, and ensemble, Logistic Regression performed better. As for related medical variables like parathyroid surgery, urea reduction ratio, etc., they play a much smaller role in mortality risk factors than diabetes and cardiovascular disease.

Author Biographies

Dony Novaliendry, Universitas Negeri Padang, Padang, Indonesia; National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

 

 

Cheng-Hong Yang, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

 

 

Yenny Desnelita, Institute Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia

 

 

Irwan, Institute Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia

 

 

Roni Sanjaya, Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia

 

 

Gustientiedina, Institute Bisnis dan Teknologi Pelita Indonesia, Pekanbaru, Indonesia

 

 

Yaslinda Lizar, Universitas Islam Negeri Imam Bonjol, Padang, Indonesia

 

 

Cover iJOE – Vol 19, No 09, 2023

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Published

2023-07-07

How to Cite

Novaliendry, D. ., Oktoria, Yang, C.-H., Desnelita, Y., Irwan, Sanjaya, R., … Ardi, N. (2023). Hemodialysis Patient Death Prediction Using Logistic Regression. International Journal of Online and Biomedical Engineering (iJOE), 19(09), pp. 66–80. https://doi.org/10.3991/ijoe.v19i09.40917

Issue

Vol. 19 No. 09 (2023)

Section

Papers

License

Copyright (c) 2023 Dony Novaliendry, Cheng-Hong Yang, Yenny Desnelita, Irwan , Roni Sanjaya, Gustientiedina, Yaslinda Lizar

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