Estimating breast cancer risks using neural networks
本文报告了用神经网络诊断乳腺癌的结果，可直接估计恶性概率，并结合重抽样技术获得概率分布，帮助研究者了解估计概率的变异性。
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 in some detail and illustrated with the application to breast cancer diagnosis.