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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Acknowledgments
This work was supported in part by the Spanish Inter-Ministerial Commission of Science and Technology under Project TIN2011-22794, the European Regional Development fund, and the “Junta de Andalucía” (Spain), under Project P2011-TIC-7508. M. Cruz-Ramírez’s research has been subsidized by the FPU Predoctoral Program (Spanish Ministry of Education and Science) with Grant reference AP2009-0487. Finally, we would like to thank Astellas Pharma Company for their partial support and the Editor and the Reviewers for their helpful suggestions for the paper.
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This paper is a significant extension of the work “Memetic Pareto differential evolutionary neural network for donor-recipient matching in liver transplantation” appearing in the International Work-Conference on Artificial Neural Networks 2011 (IWANN’11).
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Cruz-Ramírez, M., Hervás-Martínez, C., Gutiérrez, P.A. et al. Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem. Soft Comput 17, 275–284 (2013). https://doi.org/10.1007/s00500-012-0892-7
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DOI: https://doi.org/10.1007/s00500-012-0892-7