An ensemble approach for ordinal threshold models applied to liver transplantation

Áreas de investigación:

• Methodology - Machine learning

Año:

2012

Tipo de publicación:

Artículo en conferencia

Palabras clave:

ordinal regresion, ensemble, discriminant analysis, kernel methods, liver transplantation

Autores:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César
• Briceño, Javier
• de la Mata, M.

Título del libro:

2012 International Joint Conference on Neural Networks (IJCNN)

Páginas:

2795-2802

BibTex:

@conference{ordinalThresholdPerez2012, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Javier Brice{\~n}o and de la Mata, M.", abstract = "This paper proposes a novel algorithm for ordinal classification based on combining ensemble techniques and discriminant analysis. The proposal is applied to a real application of liver transplantation, where the objective is to predict survival rates of the graft. Ordinal classification is used for this problem because the classes are defined by the following temporal order: 1) failure of the graft before the first 15 days after transplantation, 2) failure between 15 days and 3 months, 3) failure between 3 months and one year, and 4) no failure presented (taking into account that the patient follow-up is up to one year after the transplantation). When compared to other state-of-the-art classifiers like AdaBoost, EBC(SVM) or KDLOR, the proposed algorithm is shown to be competitive. The models obtained could allow medical experts to predict survival rates without knowing exactly the number of days the transplanted organ survived. ", booktitle = "2012 International Joint Conference on Neural Networks (IJCNN)", doi = "10.1109/IJCNN.2012.6252755", keywords = "ordinal regresion, ensemble, discriminant analysis, kernel methods, liver transplantation", pages = "2795-2802", title = "{A}n ensemble approach for ordinal threshold models applied to liver transplantation", url = "http://dx.doi.org/10.1109/IJCNN.2012.6252755", year = "2012", }

Abstract:

This paper proposes a novel algorithm for ordinal classification based on combining ensemble techniques and discriminant analysis. The proposal is applied to a real application of liver transplantation, where the objective is to predict survival rates of the graft. Ordinal classification is used for this problem because the classes are defined by the following temporal order: 1) failure of the graft before the first 15 days after transplantation, 2) failure between 15 days and 3 months, 3) failure between 3 months and one year, and 4) no failure presented (taking into account that the patient follow-up is up to one year after the transplantation). When compared to other state-of-the-art classifiers like AdaBoost, EBC(SVM) or KDLOR, the proposed algorithm is shown to be competitive. The models obtained could allow medical experts to predict survival rates without knowing exactly the number of days the transplanted organ survived.