The Application of Rough Sets-Based Data Mining Technique to Differential Diagnosis of Meningoenchepahlitis
The Application of Rough Sets-Based Data Mining Technique to Differential Diagnosis of Meningoenchepahlitis
复制标题
基于粗糙集的数据挖掘技术在脑膜脑炎鉴别诊断中的应用
DOI：
10.1007/3-540-61286-6_168
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
W. Ziarko
中科院分区：
文献类型：
--
作者：
S. Tsumoto;W. Ziarko
描述了基于粗糙集的数据挖掘工具 KDD-R 在临床数据分析中的应用。数据挖掘技术解决的主要实际问题是细菌性脑膜脑炎和病毒性脑膜脑炎的鉴别诊断。我们介绍了 KDD-R 系统底层的变精度粗糙集模型的相关方面、KDD-R 的基本操作阶段以及领域专家进行的结果及其临床解释。
The application of rough sets-based data mining tool called KDD-R to the analysis of clinical data is described. The main practical problem tackled with the data mining technique is a differential diagnosis of bacterial versus viral meningoenchephalis. We present the relevant aspects of the variable precision rough sets model underlying the system KDD-R, the basic operational stages of KDD-R, and the results and their clinical interpretation conducted by the domain expert.