Assessing the Feasibility of Data Mining Techniques for Early Liver Cancer Detection

被引：2

作者：

Kuo, Mu-Hsing ^{[1
]}

Hung, Chang-Mao ^{[2
]}

Barnett, Jeff ^{[3
]}

Pinheiro, Fabiola

机构：

[1] Sch Hlth Informat Sci, POB 3050 STN CSC, Victoria, BC V8W 3P4, Canada

[2] Yungta Inst Technol & Commerce, Pingtung, Taiwan

[3] BC Canc Agcy, Victoria, BC, Canada

来源：

QUALITY OF LIFE THROUGH QUALITY OF INFORMATION | 2012年 / 180卷

关键词：

Data Mining; FP Growth Algorithm; Live Cancer; Association Rules; MODEL;

D O I：

10.3233/978-1-61499-101-4-584

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

The objective of this study is to assess the feasibility of a data mining association analysis technique, the FP Growth algorithm, for the detection of associations of liver cancer, geographic location and demographic of patients. For the research, we are planning to use data extracted from electronic health record systems of three healthcare organizations in different geographic locations (Canada, Taiwan and Mongolia). The data are arranged into 'transactions' which contain a set of data items focused around cancer diseases, geographic locations and patient demographics. This analysis produces association rules that indicate what combinations of demographics, geographic locations and patient characteristics lead to liver cancer.

引用

页码：584 / 588

页数：5

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