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Multi-objective evolutionary algorithm for donor–recipient decision system in liver transplants

Author

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  • Cruz-Ramı´rez, Manuel
  • Hervás-Martı´nez, César
  • Fernández, Juan Carlos
  • Briceño, Javier
  • de la Mata, Manuel

Abstract

This paper reports on a decision support system for assigning a liver from a donor to a recipient on a waiting-list that maximises the probability of belonging to the survival graft class after a year of transplant and/or minimises the probability of belonging to the non-survival graft class in a two objective framework. This is done with two models of neural networks for classification obtained from the Pareto front built by a multi-objective evolutionary algorithm – called MPENSGA2. This type of neural network is a new model of the generalised radial basis functions for obtaining optimal values in C (Correctly Classified Rate) and MS (Minimum Sensitivity) in the classifier, and is compared to other competitive classifiers. The decision support system has been proposed using, as simply as possible, those models which lead to making the correct decision about receptor choice based on efficient and impartial criteria.

Suggested Citation

  • Cruz-Ramı´rez, Manuel & Hervás-Martı´nez, César & Fernández, Juan Carlos & Briceño, Javier & de la Mata, Manuel, 2012. "Multi-objective evolutionary algorithm for donor–recipient decision system in liver transplants," European Journal of Operational Research, Elsevier, vol. 222(2), pages 317-327.
    • Handle: RePEc:eee:ejores:v:222:y:2012:i:2:p:317-327
    DOI: 10.1016/j.ejor.2012.05.013
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    References listed on IDEAS

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    1. Syberfeldt, Anna & Ng, Amos & John, Robert I. & Moore, Philip, 2010. "Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling," European Journal of Operational Research, Elsevier, vol. 204(3), pages 533-544, August.
    2. Niki Kunene, K. & Roland Weistroffer, H., 2008. "An approach for predicting and describing patient outcome using multicriteria decision analysis and decision rules," European Journal of Operational Research, Elsevier, vol. 185(3), pages 984-997, March.
    3. Branke, Juergen & Pickardt, Christoph W., 2011. "Evolutionary search for difficult problem instances to support the design of job shop dispatching rules," European Journal of Operational Research, Elsevier, vol. 212(1), pages 22-32, July.
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