Memetic Evolutionary Multi-Objective Neural Network Classifier to Predict Graft Survival in Liver Transplant Patients
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- Research areas:
- Year:
- 2011
- Type of Publication:
- In Proceedings
- Keywords:
- Artificial neural networks, Generalized radial basis functions, Liver transplantation, Multi-objective evolutionary algorithm
- Authors:
-
- Cruz-Ramírez, Manuel
- Fernández, Juan Carlos
- Fernandez-Navarro, Francisco
- Briceño, Javier
- de la Mata, Manuel
- Hervás-Martínez, César
- Book title:
- Proceedings of the 13th annual conference companion on Genetic and evolutionary computation (GECCO2011)
- Pages:
- 479-486
- Organization:
- Dublin, Ireland
- Month:
- 12-16 July
- ISBN:
- 978-1-4503-0690-4
- BibTex:
- Abstract:
- In liver transplantation, matching donor and recipient is a problem that can be solved using machine learning techniques. In this paper we consider a liver transplant dataset obtained from eleven Spanish hospitals, including the patient survival or the rejection in liver transplantation one year after it. To tackle this problem, we use a multi-objective evolutionary algorithm for training generalized radial basis functions neural networks. The obtained models provided medical experts with a mathematical value to predict survival rates allowing them to come up with a right decision according to the principles of justice, efficiency and equity.