Hemodialysis Patient Death Prediction Using Logistic Regression

被引:2
|
作者
Novaliendry, Dony [1 ,2 ]
Oktoria [1 ,2 ]
Yang, Cheng-Hong [2 ]
Desnelita, Yenny [3 ]
Irwan [3 ]
Sanjaya, Roni [3 ]
Gustientiedina [3 ]
Lizar, Yaslinda [4 ]
Ardi, Noper [5 ]
机构
[1] Univ Negeri Padang, Padang, Indonesia
[2] Natl Kaohsiung Univ Sci & Technol, Kaohsiung, Taiwan
[3] Inst Bisnis dan Teknol Pelita Indonesia, Pekanbaru, Indonesia
[4] Univ Islam Negeri Imam Bonjol, Padang, Indonesia
[5] Politekn Negeri Batam, Batam, Indonesia
关键词
logistic regression; diabetes; hemodialysis; prediction; CHRONIC KIDNEY-DISEASE;
D O I
10.3991/ijoe.v19i09.40917
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:66 / 80
页数:15
相关论文
共 50 条
  • [41] Logistic Regression for LDPC Decoding Failure Prediction
    Kim, Taehyun
    Park, JooSung
    2021 IEEE 18TH ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC), 2021,
  • [42] A logistic regression model for the prediction of endometriosis.
    Stegmann, BJ
    Sinaii, N
    Funk, MLJ
    Hartmann, KE
    Merino, M
    Segars, J
    Nieman, LK
    Stratton, P
    JOURNAL OF THE SOCIETY FOR GYNECOLOGIC INVESTIGATION, 2006, 13 (02) : 278A - 279A
  • [43] A dynamic logistic regression for network link prediction
    Zhou Jing
    Huang DanYang
    Wang HanSheng
    SCIENCE CHINA-MATHEMATICS, 2017, 60 (01) : 165 - 176
  • [44] A dynamic logistic regression for network link prediction
    Jing Zhou
    DanYang Huang
    HanSheng Wang
    Science China Mathematics, 2017, 60 : 165 - 176
  • [45] The logistic regression model for cardiac remodeling prediction
    Markelov, Oleg
    Pyko, Svetlana
    Bogachev, Mikhail
    Polyakova, Anzhelika
    Gudkova, Alexandra
    EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, 2019, 49 : 155 - 156
  • [46] A dynamic logistic regression for network link prediction
    ZHOU Jing
    HUANG DanYang
    WANG HanSheng
    ScienceChina(Mathematics), 2017, 60 (01) : 165 - 176
  • [47] Steganalysis using Logistic Regression
    Lubenko, Ivans
    Ker, Andrew D.
    MEDIA WATERMARKING, SECURITY, AND FORENSICS III, 2011, 7880
  • [48] Forest cover dynamics analysis and prediction modeling using logistic regression model
    Kumar, Rakesh
    Nandy, S.
    Agarwal, Reshu
    Kushwaha, S. P. S.
    ECOLOGICAL INDICATORS, 2014, 45 : 444 - 455
  • [49] Advancing Breast Cancer Prediction using Logistic Regression and Machine Learning Techniques
    Bhuria, Ruchika
    Gill, Kanwarpartap Singh
    Malhotra, Sonal
    Singh, Mukesh
    2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE COMPUTING AND SMART SYSTEMS, ICSCSS 2024, 2024, : 1374 - 1377
  • [50] Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks
    Tamir, Tariku Sinshaw
    Xiong, Gang
    Li, Zhishuai
    Tao, Hao
    Shen, Zhen
    Hu, Bin
    Menkir, Heruye Mulugeta
    IFAC PAPERSONLINE, 2020, 53 (05): : 512 - 517