用于医学诊断推理的前向神经网络的构造算法
A Constructive Algorithm for Feedforward Neural Networks for Medical Diagnostic Reasoning
摘要：
本研究旨在寻找解决复杂医学诊断的替代方案，其中人类知识应以一般方式理解。成功的应用实例表明，人类的诊断能力明显不如神经诊断系统。我们的研究描述了一个具有反向传播的构造性神经网络算法，为模式分类的近最小神经网络结构的增量构造提供了一种方法。该算法从单个隐藏层中隐藏单元的最小数量开始，每次向隐藏层添加一个额外的单元以提高网络的准确性并获得神经网络的最佳大小。我们的算法进行了测试的几个基准分类问题，包括癌症1，心脏病，糖尿病具有良好的泛化能力。
英文摘要：
This research is to search for alternatives to the resolution of complex medical diagnosis where human knowledge should be apprehended in a general fashion. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic system. Our research describes a constructive neural network algorithm with backpropagation; offer an approach for the incremental construction of nearminimal neural network architectures for pattern classification. The algorithm starts with minimal number of hidden units in the single hidden layer; additional units are added to the hidden layer one at a time to improve the accuracy of the network and to get an optimal size of a neural network. Our algorithm was tested on several benchmarking classification problems including Cancer1, Heart, and Diabetes with good generalization ability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
