Memetic Evolutionary Multi-Objective Neural Network Classifier to Predict Graft Survival in Liver Transplant Patients

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Research areas:
Year:
2011
Type of Publication:
In Proceedings
Keywords:
Artificial neural networks, Generalized radial basis functions, Liver transplantation, Multi-objective evolutionary algorithm
Authors:
Book title:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (GECCO2011)
Pages:
479-486
Organization:
Dublin, Ireland
Month:
12-16 July
ISBN:
978-1-4503-0690-4
BibTex:
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.
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