Memetic Evolutionary Multi-Objective Neural Network Classifier to Predict Graft Survival in Liver Transplant Patients
Autor
Cruz-Ramírez, M.
Fernández, Juan Carlos
Fernández-Navarro, F.
Briceño Delgado, Francisco Javier
Mata, Manuel de la
Hervás-Martínez, César
Fecha
2017-02-17Materia
Artificial neural networksGeneralized radial basis functions
Liver transplantation
Multi-objective evolutionary algorithm
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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 the surgery. 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