摘要
医学诊断系统是临床诊断辅助工具,可以帮助医学专家解决复杂的医学问题.文章提出一种粗糙集理论和模糊神经网络技术相结合进行病症诊断的方法.利用粗糙集理论对大量疾病诊断信息进行属性约简,消掉冗余属性,建立有效知识库,在此基础上构建参数合适的模糊神经网络对知识库进行诊断推理,从而降低了计算量,提高了约简效率,而且具有较高的诊断准确率,系统测试结果达到了辅助医生诊断的效果.
The medical diagnosis system which is a clinical diagnosis assistant tool can help medical experts to solve the complicatedmedical problems. This paper presents a method which combines rough set theory and fuzzy neural network technology to diagnose the dis-ease. Rough set theory is used to eliminate the redundant attributes of a large number of diagnostic information, and it establishes effectiveknowledge base. On this basis, it constructs the appropriate parameters of the fuzzy neural network of the knowledge to diagnostic reason-ing diseases. It can reduce the amount of calculation, improve the efficiency of the reduction. And it has higher diagnostic accuracy. Sys-tem test results to assist doctors to diagnose the effect.
出处
《广西科技师范学院学报》
2016年第3期149-152,139,共5页
Journal of Guangxi Science & Technology Normal University
基金
安徽高校自然科学研究重点项目(KJ2015A272)
池州学院院级自然学科重点项目(2014ZRZ006)
关键词
粗糙集
模糊推理
神经网络
医疗诊断
属性约简
rough sets
fuzzy inference
neural network
medical diagnosis
attribute reduction
