Early breast cancer prognosis prediction and rule extraction using a new constructive neural network algorithm

Authors:
Leonardo Franco;José Luis Subirats;Ignacio Molina;Emilio Alba;José M. Jerez;José M. Jerez
Affiliations:
Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería en Informática, Universidad de Málaga, Málaga, Spain;Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería en Informática, Universidad de Málaga, Málaga, Spain;Departamento de Tecnología Electrónica, Escuela Técnica Superior de Ingeniería en Telecomunicación, Universidad de Málaga, Málaga, Spain;Servicio de Oncología,;Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería en Informática, Universidad de Málaga, Málaga, Spain;Servicio de Oncología, Hospital Clínico Universitario Virgen de la Victoria, Málaga, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 2
Cited 1

C4.5: programs for machine learning

C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations

A hybrid intelligent system for medical data classification

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Breast cancer relapse prediction is an important step in the complex decision-making process of deciding the type of treatment to be applied to patients after surgery. Some non-linear models, like neural networks, have been successfully applied to this task but they suffer from the problem of extracting the underlying rules, and knowing how the methods operate can help to a better understanding of the cancer relapse problem. A recently introduced constructive algorithm (DASG) that creates compact neural network architectures is applied to a dataset of early breast cancer patients with the aim of testing the predictive ability of the new method. The DASG method works with Boolean input data and for that reason a transformation procedure was applied to the original data. The degradation in the predictive performance due to the transformation of the data is also analyzed using the new method and other standard algorithms.