Extracting association rules from liver cancer data using the FP-growth algorithm

Author

Pinheiro, Fabiola ; Mu-Hsing Kuo ; Thomo, Alex ; Barnett, Jeff

Author_Institution

Sch. of Health Inf. Sci., Univ. of Victoria, Victoria, BC, Canada

fYear

2013

fDate

12-14 June 2013

Firstpage

1

Lastpage

1

Abstract

The five-year survival rate of liver cancer is low, 14% according to the Surveillance, Epidemiology, and End Results (SEER) Program database of the National Cancer Institute from 2003 to 2007 [3]. Since in the early stages of liver cancer, patients usually do not show signs or symptoms, improving early diagnosis is essential in order to reduce morbidity and mortality rates. Association rule mining, a popular method for discovering interesting hidden relationships or patterns between variables in large databases, has demonstrated benefit when applied to cancer detection and management. To date, however, no studies have applied it to liver cancer. The objective of this study was to apply the FP-growth association algorithm to discover patterns from liver cancer data, which can hopefully be used for early detection.

Keywords

cancer; data mining; feature extraction; liver; medical diagnostic computing; patient diagnosis; FP-growth association algorithm; National Cancer Institute; Surveillance-Epidemiology-and-End Results Program database; association rule extraction; association rule mining; cancer detection; cancer management; diagnosis; five-year survival rate; liver cancer data; morbidity rates; mortality rates; time 5 yr; Association rules; Cancer; Data analysis; Databases; Educational institutions; Liver; FP-growth algorithm; association rules; data mining; liver cancer;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Advances in Bio and Medical Sciences (ICCABS), 2013 IEEE 3rd International Conference on

Conference_Location

New Orleans, LA

Type

conf

DOI

10.1109/ICCABS.2013.6629208

Filename

6629208