%0 Journal Article
%T Comparing Three Data Mining Methods to Predict Kidney Transplant Survival
%A Alireza Borhani
%A Gholamreza Pourmand
%A Leila Shahmoradi
%A Mostafa Langarizadeh
%A Ziba Aghsaei fard
%J Archive of "Acta Informatica Medica".
%D 2016
%R 10.5455/aim.2016.24.322-327
%X One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant
%K data mining
%K survival
%K kidney transplantation
%K C5.0 algorithm
%K C&RTree algorithm
%K neural network algorithm
%K CRISP methodology
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5256037/