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Reseach Article

Recent Trends in Computational Prediction of Renal Transplantation Outcomes

by Aswathy Ravikumar, Saritha R, Vinod Chandra S S
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 12
Year of Publication: 2013
Authors: Aswathy Ravikumar, Saritha R, Vinod Chandra S S
10.5120/10521-5501

Aswathy Ravikumar, Saritha R, Vinod Chandra S S . Recent Trends in Computational Prediction of Renal Transplantation Outcomes. International Journal of Computer Applications. 63, 12 ( February 2013), 33-37. DOI=10.5120/10521-5501

@article{ 10.5120/10521-5501,
author = { Aswathy Ravikumar, Saritha R, Vinod Chandra S S },
title = { Recent Trends in Computational Prediction of Renal Transplantation Outcomes },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 12 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number12/10521-5501/ },
doi = { 10.5120/10521-5501 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:11.161610+05:30
%A Aswathy Ravikumar
%A Saritha R
%A Vinod Chandra S S
%T Recent Trends in Computational Prediction of Renal Transplantation Outcomes
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 12
%P 33-37
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Renal transplantation has become the treatment of choice for most patients with end-stage renal disease. Recent advances in renal transplantation notably, the matching of Major Histocompatibility Complex (MHC) and improved immunosuppressants have improved short-term and long-term graft survival rates. In light of recent developments optimization of kidney transplant outcomes is paramount to further augment the graft survival time and the quality of life of the patient. An intuitive understanding of the post transplantation interaction mechanisms involving graft and host is intricate and on account of this prognosis of planned organ transplantation outcomes is an involved problem. Consequently, machine learning approaches based on donor and recipient data are indespensible for improved prognosis of graft outcomes. This study proposes improved data mining-based models for variable filtering and for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation.

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Index Terms

Computer Science
Information Sciences

Keywords

Prediction model Survival analysis machine learning Data mining Renal Transplantation