预测乳腺癌复发事件的神经计算方法的统计评价

F. Gorunescu, M. Gorunescu, E. El-Darzi, S. Gorunescu
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引用次数: 12

摘要

乳腺癌被认为是当今妇女癌症死亡的第二大原因。有时,乳腺癌在初次治疗后会复发。癌症复发的医学诊断往往比最初的诊断更具挑战性。在本文中,我们研究了智能神经网络作为支持卫生专业人员诊断此类事件的有用工具的潜在贡献。神经网络算法应用于从卢布尔雅那肿瘤研究所获得的乳腺癌数据集。为了验证我们的实验,进行了广泛的统计分析。结果表明,对于多层感知和径向基函数,一个简单的网络结构都可以产生同样好的结果,这两种算法都不需要所有的属性来训练,并且两种算法的分类性能在统计上都是鲁棒的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A statistical evaluation of neural computing approaches to predict recurrent events in breast cancer
Breast cancer is considered to be the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often more challenging task than the initial one. In this paper we investigate the potential contribution of intelligent neural networks as a useful tool to support health professionals in diagnosing such events. The neural network algorithms are applied to the breast cancer dataset obtained from Ljubljana Oncology Institute. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perception and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and finally, the classification performances of both algorithms are statistically robust.
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