School of Computing Sciences, Central Queensland University, Queensland, Australia.
J Med Syst. 2011 Jun;35(3):353-67. doi: 10.1007/s10916-009-9372-8. Epub 2009 Oct 3.
Cancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer. To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.
癌症正在增加全球意外死亡的总数。到目前为止，癌症研究还不能为癌症患者提供有效的解决方案，因此，高死亡率仍未得到控制。本研究旨在提取特定类型癌症的显著预防因素。为了找出这些预防因素，我们首先通过广泛的文献回顾，构建了一个膀胱癌、乳腺癌、宫颈癌、肺癌、前列腺癌和皮肤癌的预防因素数据集。随后，我们采用了三种关联规则挖掘算法，即 Apriori、Predictive apriori 和 Tertius 算法，以发现针对这些特定类型癌症的大部分显著预防因素。实验结果表明，Apriori 是发现预防因素时最有用的关联规则挖掘算法。
