Multi-Objective Evolutionary Algorithm for Donor-Recipient Decision System in Liver Transplants

Research areas:

• Methodology - Machine learning

Year:

2012

Type of Publication:

Article

Keywords:

Artificial Neural Networks, Generalized Radial Basis Function, Liver Transplantation, Multi-Objective Evolutionary Algorithms, Organ allocations, Rule-based System

Authors:

• Cruz-Ramírez, Manuel
• Hervás-Martínez, César
• Fernández, Juan Carlos
• Briceño, Javier
• de la Mata, Manuel

Journal:

European Journal of Operational Research

Volume:

222

Number:

2

Pages:

317-327

Month:

October

ISSN:

0377-2217

BibTex:

@article{mcruzEJOR2012, author = "Manuel Cruz-Ram{\'i}rez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Juan Carlos Fern{\'a}ndez and Javier Brice{\~n}o and de la Mata, Manuel", abstract = "This paper reports on a decision support system for assigning a liver from a donor to a recipient on a waiting-list that maximises the probability of belonging to the survival graft class after a year of transplant and/or minimises the probability of belonging to the non-survival graft class in a two objective framework. This is done with two models of neural networks for classification obtained from the Pareto front built by a multi-objective evolutionary algorithm –called MPENSGA2. This type of neural network is a new model of the generalized radial basis functions for obtaining optimal values in C (Correctly Classified Rate) and MS (Minimum Sensitivity) in the classifier, and is compared to other competitive classifiers. The decision support system has been proposed using, as simply as possible, those models which lead to making the correct decision about receptor choice based on efficient and impartial criteria.", awards = "JCR (2012): 2.038 Position: 9/79 (Q1) Category: OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE", comments = "JCR (2012): 2.038 Position: 9/79 (Q1) Category: OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE", doi = "10.1016/j.ejor.2012.05.013", issn = "0377-2217", journal = "European Journal of Operational Research", keywords = "Artificial Neural Networks, Generalized Radial Basis Function, Liver Transplantation, Multi-Objective Evolutionary Algorithms, Organ allocations, Rule-based System", month = "October", note = "JCR (2012): 2.038 Position: 9/79 (Q1) Category: OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE", number = "2", pages = "317--327", title = "{M}ulti-{O}bjective {E}volutionary {A}lgorithm for {D}onor-{R}ecipient {D}ecision {S}ystem in {L}iver {T}ransplants", url = "http://dx.doi.org/10.1016/j.ejor.2012.05.013", volume = "222", year = "2012", }

Note:

JCR (2012): 2.038 Position: 9/79 (Q1) Category: OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Abstract:

This paper reports on a decision support system for assigning a liver from a donor to a recipient on a waiting-list that maximises the probability of belonging to the survival graft class after a year of transplant and/or minimises the probability of belonging to the non-survival graft class in a two objective framework. This is done with two models of neural networks for classification obtained from the Pareto front built by a multi-objective evolutionary algorithm –called MPENSGA2. This type of neural network is a new model of the generalized radial basis functions for obtaining optimal values in C (Correctly Classified Rate) and MS (Minimum Sensitivity) in the classifier, and is compared to other competitive classifiers. The decision support system has been proposed using, as simply as possible, those models which lead to making the correct decision about receptor choice based on efficient and impartial criteria.

Comments:

JCR (2012): 2.038 Position: 9/79 (Q1) Category: OPERATIONS RESEARCH & MANAGEMENT SCIENCE