title: Assessing the feasibility of data mining techniques for early liver cancer detection.
abstract: 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.
authors.affiliation: School of Health Information Science, University of Victoria, BC, Canada. akuo@uvic.ca
authors.forename: Mu-Hsing
authors.initials: MH
authors.lastname: Kuo
authors.forename: Chang-Mao
authors.initials: CM
authors.lastname: Hung
authors.forename: Jeff
authors.initials: J
authors.lastname: Barnett
authors.forename: Fabiola
authors.initials: F
authors.lastname: Pinheiro
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D055088:Early Detection of Cancer; D005240:Feasibility Studies; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000453:epidemiology*; D015995:Prevalence; D018570:Risk Assessment