@conference{Gecco2011,
author = "Manuel Cruz-Ram{\'i}rez and Juan Carlos Fern{\'a}ndez and Francisco Fernandez-Navarro and Javier Brice{\~n}o and de la Mata, Manuel and C{\'e}sar Herv{\'a}s-Mart{\'i}nez",
abstract = "In liver transplantation, matching donor and recipient is a problem that can be solved using machine learning techniques. In this paper we consider a liver transplant dataset obtained from eleven Spanish hospitals, including the patient survival or the rejection in liver transplantation one year after it. To tackle this problem, we use a multi-objective evolutionary algorithm for training generalized radial basis functions neural networks. The obtained models provided medical experts with a mathematical value to predict survival rates allowing them to come up with a right decision according to the principles of justice, efficiency and equity.",
booktitle = "Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (GECCO2011)",
doi = "10.1145/2001858.2002037",
isbn = "978-1-4503-0690-4",
keywords = "Artificial neural networks, Generalized radial basis functions, Liver transplantation, Multi-objective evolutionary algorithm",
month = "12-16 July",
organization = "Dublin, Ireland",
pages = "479-486",
title = "{M}emetic {E}volutionary {M}ulti-{O}bjective {N}eural {N}etwork {C}lassifier to {P}redict {G}raft {S}urvival in {L}iver {T}ransplant {P}atients",
url = "http://dx.doi.org/10.1145/2001858.2002037",
year = "2011",
}