Assessing the feasibility of data mining techniques for early liver cancer detection

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 electron...

Full description

Saved in:
Bibliographic Details
Published inStudies in health technology and informatics Vol. 180; p. 584
Main Authors Kuo, Mu-Hsing, Hung, Chang-Mao, Barnett, Jeff, Pinheiro, Fabiola
Format Journal Article
LanguageEnglish
Published Netherlands 2012
Subjects
Algorithms
Data Mining - methods
Databases, Factual
Decision Support Systems, Clinical
Diagnosis, Computer-Assisted - methods
Early Detection of Cancer
Feasibility Studies
Humans
Liver Neoplasms - diagnosis
Liver Neoplasms - epidemiology
Prevalence
Risk Assessment
Online AccessGet more information
ISSN0926-9630
DOI10.3233/978-1-61499-101-4-584

Cover

More Information
Summary: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.
ISSN:0926-9630
DOI:10.3233/978-1-61499-101-4-584