Ordinal Evolutionary Artificial Neural Networks for Solving an Imbalanced Liver Transplantation Problem

Abstract

Ordinal regression considers classification problems where there exists a natural ordering among the categories. In this learning setting, thresholds models are one of the most used and successful techniques. On the other hand, liver transplantation is a widely-used treatment for patients with a terminal liver disease. This paper considers the survival time of the recipient to perform an appropriate donor-recipient matching, which is a highly imbalanced classification problem. An artificial neural network model applied to ordinal classification is used, combining evolutionary and gradient-descent algorithms to optimize its parameters, together with an ordinal over-sampling technique. The evolutionary algorithm applies a modified fitness function able to deal with the ordinal imbalanced nature of the dataset. The results show that the proposed model leads to competitive performance for this problem.