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Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients

Published:12 July 2011Publication History

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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    • Published in

      cover image ACM Conferences
      GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
      July 2011
      1548 pages
      ISBN:9781450306904
      DOI:10.1145/2001858

      Copyright © 2011 ACM

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      Publication History

      • Published: 12 July 2011

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