改进关联规则算法对乳腺癌扩散的预测研究  

Prediction of Breast Cancer Diffusion by Improved Association Rule Algorithm

作　　者：艾云昊 杨超宇[1] 李慧宗[1] AI Yunhao;YANG Chaoyu;LI Huizong(Anhui University of Science and Technology,Huainan 232001,Anhui,China)

机构地区：[1]安徽理工大学,安徽淮南232001

出　　处：《江汉大学学报（自然科学版）》2020年第4期72-79,共8页Journal of Jianghan University：Natural Science Edition

基　　金：国家自然科学基金资助项目(61873004,51874003);安徽省自然科学基金面上项目(1808085MG221);安徽理工大学博士基金资助项目(11892)。

摘　　要：传统Apriori算法只能处理布尔型数据,无法对包含连续属性的乳腺癌患者就诊记录进行规则挖掘。对此,提出一种基于改进Apriori算法的乳腺癌扩散的预测方法。该方法通过引入模糊集理论,提出新的支持度计算方法,对Apriori算法进行优化。实验结果表明,改进后的算法能够处理含有连续型数据的乳腺癌患者就诊记录,相比传统算法,能够挖掘出更多、质量更高的规则,得出了乳腺癌患者的致病因素和扩散之间的隐藏规则,从而验证了改进后的Apriori算法对于辅助乳腺癌患者治疗具有指导意义。Traditional Apriori algorithm can only deal with Boolean data and can’t process the medical records of breast cancer patients with continuous data. In this regard, a prediction method of breast cancer diffusion based on the improved Apriori algorithm is proposed,which based on a new support calculation method including the fuzzy set theory.Experimental results show that the improved algorithm can process records of breast cancer patients with continuous data. Compared with traditional algorithms,it can mine more and higher quality rules,and obtain the hidden rules between the pathogenic factors and diffusion of breast cancer patients. It verifies that the improved Apriori algorithm is of guiding significance for the treatment of breast cancer patients.

关 键 词：数据挖掘 APRIORI算法 乳腺癌扩散预测 模糊集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]