Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients
Joint Authors
Rezapour, Mohammad
Sepehri, Mohammad Mehdi
Khavanin Zadeh, Morteza
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-04
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Arteriovenous fistula (AVF) is an important vascular access for hemodialysis (HD) treatment but has 20–60% rate of early failure.
Detecting association between patient's parameters and early AVF failure is important for reducing its prevalence and relevant costs.
Also predicting incidence of this complication in new patients is a beneficial controlling procedure.
Patient safety and preservation of early AVF failure is the ultimate goal.
Our research society is Hasheminejad Kidney Center (HKC) of Tehran, which is one of Iran's largest renal hospitals.
We analyzed data of 193 HD patients using supervised techniques of data mining approach.
There were 137 male (70.98%) and 56 female (29.02%) patients introduced into this study.
The average of age for all the patients was 53.87 ± 17.47 years.
Twenty eight patients had smoked and the number of diabetic patients and nondiabetics was 87 and 106, respectively.
A significant relationship was found between “diabetes mellitus,” “smoking,” and “hypertension” with early AVF failure in this study.
We have found that these mentioned risk factors have important roles in outcome of vascular surgery, versus other parameters such as “age.” Then we predicted this complication in future AVF surgeries and evaluated our designed prediction methods with accuracy rates of 61.66%–75.13%.
American Psychological Association (APA)
Rezapour, Mohammad& Khavanin Zadeh, Morteza& Sepehri, Mohammad Mehdi. 2013. Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-501589
Modern Language Association (MLA)
Rezapour, Mohammad…[et al.]. Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-501589
American Medical Association (AMA)
Rezapour, Mohammad& Khavanin Zadeh, Morteza& Sepehri, Mohammad Mehdi. Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-501589
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-501589