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Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem

  • Manuel Dorado-MorenoEmail author
  • María Pérez-Ortiz
  • María Dolores Ayllón-Terán
  • Pedro Antonio Gutiérrez
  • Cesar Hervás-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

Ordinal regression considers classification problems where there exists a natural ordering among the categories. In this learning setting, thresholds models are one of the most used and successful techniques. On the other hand, liver transplantation is a widely-used treatment for patients with a terminal liver disease. This paper considers the survival time of the recipient to perform an appropriate donor-recipient matching, which is a highly imbalanced classification problem. An artificial neural network model applied to ordinal classification is used, combining evolutionary and gradient-descent algorithms to optimize its parameters, together with an ordinal over-sampling technique. The evolutionary algorithm applies a modified fitness function able to deal with the ordinal imbalanced nature of the dataset. The results show that the proposed model leads to competitive performance for this problem.

Keywords

Ordinal regression Artificial neural networks Imbalanced classification Liver transplantation Donor-recipient matching 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Manuel Dorado-Moreno
    • 1
    Email author
  • María Pérez-Ortiz
    • 2
    • 3
  • María Dolores Ayllón-Terán
    • 3
  • Pedro Antonio Gutiérrez
    • 1
  • Cesar Hervás-Martínez
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CórdobaCórdobaSpain
  2. 2.Department of Mathematics and EngineeringUniversidad Loyola AndalucíaCórdobaSpain
  3. 3.Liver Transplantation Unit, Reina Sofía HospitalCórdobaSpain

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