Memetic Pareto Differential Evolutionary Neural Network for Donor-Recipient Matching in Liver Transplantation

Research areas:

• Methodology - Machine learning

Year:

2011

Type of Publication:

In Proceedings

Authors:

• Cruz-Ramírez, Manuel
• Hervás-Martínez, César
• Gutiérrez, Pedro Antonio
• Briceño, Javier
• de la Mata, Manuel

Volume:

II

Book title:

11th international conference on Artificial neural networks conference (IWANN'11)

Series:

Advances in Computational Intelligence

Pages:

129-136

ISBN:

978-3-642-21497-4

BibTex:

@conference{IWANN2011, author = "Manuel Cruz-Ram{\'i}rez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Pedro Antonio Guti{\'e}rrez and Javier Brice{\~n}o and de la Mata, Manuel", abstract = "Donor-Recipient matching constitutes a complex scenario not easily modelable. The risk of subjectivity and the likelihood of falling into error must not be underestimated. Computational tools for decisionmaking process in liver transplantation can be useful, despite its inherent complexity. Therefore, a Multi-Objective Evolutionary Algorithm and various techniques of selection of individuals are used in this paper to obtain Artificial Neural Network models to assist in making decisions. Thus, the experts will have a mathematical value that enables them to make a right decision without deleting the principles of justice, efficiency and equity.", booktitle = "11th international conference on Artificial neural networks conference (IWANN{\&}#039;11)", isbn = "978-3-642-21497-4", pages = "129-136", publisher = "Springer-Verlag Berlin, Heidelberg ©2011 ", series = "Advances in Computational Intelligence", title = "{M}emetic {P}areto {D}ifferential {E}volutionary {N}eural {N}etwork for {D}onor-{R}ecipient {M}atching in {L}iver {T}ransplantation", volume = "II", year = "2011", }

Abstract:

Donor-Recipient matching constitutes a complex scenario not easily modelable. The risk of subjectivity and the likelihood of falling into error must not be underestimated. Computational tools for decisionmaking process in liver transplantation can be useful, despite its inherent complexity. Therefore, a Multi-Objective Evolutionary Algorithm and various techniques of selection of individuals are used in this paper to obtain Artificial Neural Network models to assist in making decisions. Thus, the experts will have a mathematical value that enables them to make a right decision without deleting the principles of justice, efficiency and equity.