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。

关键词: 最优风险与预防模型, 数据挖掘, 权重, 风险打分矩阵, 预防打分矩阵

Abstract: 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.

Key words: optimal risk and prevention model, data mining, weight, risk scoring matrix, prevention scoring matrix

中图分类号: 

• TP393