江汉大学学报(自然科学版) ›› 2020, Vol. 48 ›› Issue (4): 72-79.doi: 10.16389/j.cnki.cn42-1737/n.2020.04.010

• 计算机与信息科学 • 上一篇    下一篇

改进关联规则算法对乳腺癌扩散的预测研究

艾云昊,杨超宇,李慧宗   

  1. 安徽理工大学,安徽 淮南 232001
  • 发布日期:2020-08-06
  • 作者简介:艾云昊(1997— ),男,硕士生,研究方向:数据挖掘。
  • 基金资助:
    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)

Prediction of Breast Cancer Diffusion by Improved Association Rule Algorithm

AI Yunhao,YANG Chaoyu,LI Huizong   

  1. Anhui University of Science and Technology,Huainan 232001,Anhui,China
  • Published:2020-08-06

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。

关键词: 数据挖掘, Apriori 算法, 乳腺癌扩散预测, 模糊集

Abstract: 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.

Key words: data mining, Apriori algorithm, breast cancer diffusion prediction, fuzzy sets

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