Hybrid Multi-objective Machine Learning Classification in Liver Transplantation

Research areas:

• Methodology - Machine learning

Year:

2012

Type of Publication:

In Proceedings

Keywords:

multiobjective, evolutionary computation, neural networks, liver transplantation, differential evolution

Authors:

• Pérez-Ortiz, María
• Cruz-Ramírez, Manuel
• Fernández, Juan Carlos
• Hervás-Martínez, César

Volume:

7208

Book title:

7th International Conference on Hybrid Artificial Intelligence Systems (HAIS2012)

Series:

Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science

Pages:

397-408

Organization:

Salamanca, Spain

Month:

28-30 March

Abstract:

This paper constructs a hybrid, multi-objective and evolutionary algorithm based on Differential Evolutions using neural network models and q-Gaussian basis units in order to develop an efficient and complete system for donor-recipient assignment in liver transplantation. The algorithm is used for the classification of a binary dataset and will predict graft survival at 15 and 90 days after the transplantation. Other hybrid approaches combining artificial neural networks with evolutionary computation and well-known algorithms are presented in order to compare the obtained performance of both mono and multi-objective methods, using other methods such as Support Vector Machines and Discriminant Analysis. Some supervised attribute selection methods were previously applied, in order to extract the most discriminant variables in the problem presented. The models obtained allowed medical experts to predict survival rates and to come to a fair decision based on the principles of justice, efficiency and equity.