Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients

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 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.