使用Logistic回归预测血液透析患者死亡

IF 1.7 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Online and Biomedical Engineering Pub Date : 2023-07-07 DOI:10.3991/ijoe.v19i09.40917
D. Novaliendry, Oktoria, Cheng-Hong Yang, Y. Desnelita, Irwan, Roni Sanjaya, Gustientiedina, Yaslinda Lizar, Noper Ardi
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引用次数: 0

摘要

血液透析是一种从身体新陈代谢的废物中清除血液的程序。这是治疗终末期肾病的方法之一。这种疾病主要有两种分类,即急性肾衰竭和慢性肾衰竭。当肾脏损伤足够严重或持续很长时间时,就会发生肾衰竭,因此该疾病通常是肾脏疾病的最后阶段。透析是对肾衰竭患者进行的,包括急性肾衰竭和慢性肾衰竭。本研究旨在预测血液透析患者的死亡风险。台湾血液透析中心共招募了665名血液透析患者。预测基于逻辑回归。与K-近邻、线性判别、树和集合相比,Logistic回归表现更好。至于甲状旁腺手术、尿素还原率等相关医学变量,它们在死亡风险因素中的作用比糖尿病和心血管疾病小得多。
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Hemodialysis Patient Death Prediction Using Logistic Regression
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.
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CiteScore
4.00
自引率
46.20%
发文量
143
审稿时长
12 weeks
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