摘要
决策树是医疗数据挖掘中一种重要分类方法,针对原始医疗数据存在大量重复样本和冗余属性,影响医疗诊断的精度和速度这一问题,提出了一种基于粗糙集和ID3算法相结合的决策树方法.将所提方法应用于冠心病诊断决策,并对属性约简前后的决策性能进行了比较分析.实验表明了该方法的有效性和实用性.
It was found that the precision and speed of medical diagnosis is unsatisfactory due to large-- scale repeating data and redundant attributes in medical data in practice. To solve the problem, a new model based on rough set and decision tree ID3 is presented. The theory of rough set reduces the initial sample sets,while the decision tree ID3 is used to learn quickly the reduced decision tables and form a tree classifier. An example is given to show the validity and practicability of the new model.
基金
安徽省教育厅自然科学基金资助项目(2005kj094)
关键词
数据挖掘
粗糙集
决策树
ID3算法
医疗数据
data mining
rough set
decision tree
ID3 algorithm
medical diagnosis data
