摘要
为有效地实现疾病的早期诊断和预防,提出一种带权重的、基于最优风险与预防模型的医疗数据挖掘算法。利用最优风险与预防模型产生和疾病相关的特征属性值项,通过带权重的风险和预防集算法确定每个特征属性值项的权重。在2个标准医疗数据集中的测试结果表明,该算法能获取医疗数据中具有代表性的特征属性值项,并且每个特征属性值项都被赋予一个权重,使其获得较好的挖掘效果。
In order to efficiently make disease diagnosis and prevention,a medical data mining with weight based on optimal risk and prevention model is proposed.It generates attribute-value items associated with disease based on optimal risk and prevention model,and the weight of attribute-value items is determined by Risk and Prevention Set with Weight（RPSW） algorithm.The algorithm is tested by two benchmark medical data sets.Experimental results show that the algorithm can obtain representative attribute-value items in medical data,and each attribute-value item is assigned to a weight so as to achieve better performance.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第22期32-34,共3页
Computer Engineering
基金
云南基础应用研究基金资助项目(2009ZC049M)
昆明理工大学科学研究基金资助项目(2009-022)
