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- Title
Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem.
- Authors
Cruz-Ramírez, M.; Hervás-Martínez, C.; Gutiérrez, P.; Pérez-Ortiz, M.; Briceño, J.; de la Mata, M.
- 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.
- Subjects
LIVER transplantation; ALLOCATION of organs, tissues, etc.; DIFFERENTIAL evolution; ARTIFICIAL neural networks; PARETO analysis
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2013, Vol 17, Issue 2, p275
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-012-0892-7