摘要
为了实现从大量的医学数据中获取有效的知识并形成规则和做出正确的推理,提出了一种基于粗糙集和距离型模糊推理进行生活方式病检查的模型.采用改进的依赖度属性约简算法研究属性约简,去掉不必要属性,减小规则库规模,提高粗糙集知识发现方法在医疗健康数据上的分类效率和准确性.采用距离型模糊推理方法匹配知识库中已有规则,计算规则和给定事实的距离进行推理检查,构建距离型模糊推理的检查系统.通过实验数据描述了知识库的构建过程,并验证了模型的有效性.
In order to obtain useful knowledge from a large amount of medical data, form the rules andmake the correct reasoning, a model based on rough set and distance-type fuzzy reasoning was proposed toexamine the life style disease. The improved dependency attribute reduction algorithm was used to performthe attribute reduction, remove unnecessary attributes, reduce the rule base size and improve theclassification efficiency and accuracy of rough set knowledge discovery method in the medical and healthdata. The distance-type fuzzy reasoning method was used to match the existing rules in the knowledge base,calculate the distance between the rules and given fact for reasoning detection, and establish the detectionsystem of distance-type fuzzy reasoning. The construction process of knowledge base was described with theexperimental data, and the validity of the model was verified.
出处
《沈阳工业大学学报》
EI
CAS
北大核心
2016年第5期537-544,共8页
Journal of Shenyang University of Technology
基金
辽宁省自然科学基金资助项目(2015020010)
辽宁省高等学校优秀科技人才支持计划项目(LR2015045)
辽宁省教育厅科学研究一般项目(L2012041)
关键词
粗糙集
属性约简
距离型模糊推理
分离原则
生活方式病
疾病检查
规则提取
规则库
rough set
attribute reduction
distance-type disease
disease examination
rule extraction
fuzzy reasoning
separation principle
lifestylerule base
