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Soft Computing

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01.02.2013 | Focus

Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem

verfasst von: M. Cruz-Ramírez, C. Hervás-Martínez, P. A. Gutiérrez, M. Pérez-Ortiz, J. Briceño, M. de la Mata

Erschienen in: Soft Computing | Ausgabe 2/2013

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Abstract

Donor–recipient matching constitutes a complex scenario difficult to model. The risk of subjectivity and the likelihood of falling into error must not be underestimated. Computational tools for the decision-making process in liver transplantation can be useful, despite the inherent complexity involved. Therefore, a multi-objective evolutionary algorithm and various techniques to select individuals from the Pareto front are used in this paper to obtain artificial neural network models to aid decision making. Moreover, a combination of two pre-processing methods has been applied to the dataset to offset the existing imbalance. One of them is a resampling method and the other is a outlier deletion method. The best model obtained with these procedures (with AUC = 0.66) give medical experts a probability of graft survival at 3 months after the operation. This probability can help medical experts to achieve the best possible decision without forgetting the principles of fairness, efficiency and equity.

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Titel: Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem
verfasst von: M. Cruz-Ramírez
C. Hervás-Martínez
P. A. Gutiérrez
M. Pérez-Ortiz
J. Briceño
M. de la Mata
Publikationsdatum: 01.02.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 2/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0892-7

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