Liang Chunlin, Peng Lingxi
School of Information, Guangdong Ocean University, Zhanjiang, 524088, China,
J Med Syst. 2013 Apr;37(2):9932. doi: 10.1007/s10916-013-9932-9. Epub 2013 Mar 1.
The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.
医疗保健成本的上升是世界上最重要的问题之一。疾病预测也是一个活跃的研究领域。研究人员使用了各种技术，如支持向量机、人工神经网络等来解决这个问题。本研究通常利用免疫系统的学习和记忆特性来解决肝病诊断问题。所提出的系统应用人工免疫和遗传算法相结合的方法来诊断肝病。系统架构基于人工免疫系统。系统的学习过程采用遗传算法来干预抗体群体的进化。实验使用了我们研究中的两个基准数据集，这些数据集来自著名的UCI机器学习库。与文献中的其他诊断系统相比，所获得的诊断准确率非常可观。这些结果表明，该系统可能是一种有用的肝病自动诊断工具。
