Name
Abstract | ||
|---|---|---|
Breast cancer is one of the most important medical problems. In this paper, we report the results of using neural networks for breast cancer diagnosis. The theoretical advantage is that posterior probabilities of malignancy can be estimated directly, and coupled with resampling techniques such as the bootstrap, distributions of the probabilities can also be obtained. These allow a researcher much more insight into the variability of estimated probabilities. Another contribution is that we present an integrative approach to building neural network models. The issues of model selection, feature selection, and function approximation are discussed with some detail and illustrated with the application to breast cancer diagnosis. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1057/sj/jors/2601276 | Journal of The Operational Research Society |
Keywords | DocType | Volume |
operational research,operations research,forecasting,breast cancer,model selection,scheduling,neural network,computer science,neural network model,communications technology,feature selection,logistics,marketing,function approximation,reliability,information systems,inventory,production,project management,information technology,location,management science,posterior probability,investment | Journal | 53 |
Issue | ISSN | Citations |
2 | 0160-5682 | 8 |
PageRank | References | Authors |
0.71 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming S. Hung | 1 | 80 | 19.29 |
| Murali Shanker | 2 | 73 | 7.76 |
| Michael Y. Hu | 3 | 426 | 55.74 |