【摘要】 介绍了从医疗数据中发现关联规则的方法,分析了医疗数据的特点,并以心脏疾病诊断的数据集为例,阐述了把医疗数据转换成事务数据格式的方法,描述了关联规则挖掘在医疗数据分析中应用所遇到的难题,针对这些难题给出了一种改进的Apriori算法,并用数据进行测试。结果表明,此算法优于Apriori算法,它可以减少产生的规则的数量,从而能快速发现有趣的医疗关联规则。更多还原

【Abstract】 Mainly introduces the way to discover asso ciation rules in medical data .In this paper, characteristics of medical data ar e analyzed. An example about a small medical data set of heart disease is given to explain how to map medical data to a transaction format containing items. Be cause of some problems existing in the application of association rules in medic al data analysis,an improved Apriori algorithm is introduced to solve them. Thr ough testing it is shown from results that the algorithm outperforms Apriori alg orithm. The results show that by using this algorithm some useful medical rules are validated.更多还原