使用神经网络的医学诊断
Medical diagnosis using neural network
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。本文介绍一种改进的前馈神经网络构造算法（MFNNCA），它是一种新的医学诊断算法。新的构造算法与反向传播，提供了一种渐进式构造的近最小神经网络结构的模式分类。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。MFNNCA在几个基准分类问题上进行了测试，包括癌症，心脏病和糖尿病。实验结果表明，MFNNCA可以产生最优的神经网络结构，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. This paper describes a modified feedforward neural network constructive algorithm (MFNNCA), a new algorithm for medical diagnosis. The new constructive algorithm with backpropagation; offer an approach for the incremental construction of near-minimal 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. The MFNNCA was tested on several benchmarking classification problems including the cancer, heart disease and diabetes. Experimental results show that the MFNNCA can produce optimal neural network architecture 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。
