Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Kernel principal component analysis
Advances in kernel methods
Computational Statistics & Data Analysis
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Self-organizing map for cluster analysis of a breast cancer database
Artificial Intelligence in Medicine
Model selection for a medical diagnostic decision support system: a breast cancer detection case
Artificial Intelligence in Medicine
| Hi-index | 0.98 |
Breast screening is a method of detecting breast cancer at a very early stage. The first step involves taking an X-ray, called a mammogram, of each breast. The mammogram can detect small changes in breast tissue which may indicate cancers which are too small to be felt either by the woman herself or by a doctor. The World Health Organisation's International Agency for Research on Cancer (IARC) concluded that mammography screening for breast cancer reduces mortality. This means that out of every 500 women screened, one life will be saved. The present research uses the information obtained from the breast screening programme carried out in the public health area of Aviles (Principality of Asturias, Spain) from 1999 to 2007. The public health area of Aviles is formed by nine municipalities with a total of 160,000 inhabitants. The selection of the public health area was based on the following criteria: *This is the first screening programme performed in the area. *Almost 100% of the population in the area benefit from the public health system. *The Aviles public health area is a well-defined area of the region that does not send patients to other public health areas, which makes the study easier and more accurate. This paper describes a neural network based approach to breast cancer diagnosis; the model developed is able to determine which women are more likely to suffer from a particular kind of tumour before they undergo a mammography.