Applying the Apriori and FP-Growth Association Algorithms to Liver Cancer Data
Cancer is the leading cause of deaths globally. Although liver cancer ranks only fourth in incidence worldwide among all types of cancer, its survivability rate is the lowest. Liver cancer is often diagnosed at an advanced stage, because in the early stages of the disease patients usually do not...
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| Language: | English en |
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2013
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| Online Access: | http://hdl.handle.net/1828/4846 |
| Summary: | Cancer is the leading cause of deaths globally. Although liver cancer ranks only
fourth in incidence worldwide among all types of cancer, its survivability rate is the
lowest. Liver cancer is often diagnosed at an advanced stage, because in the early stages
of the disease patients usually do not have signs or symptoms. After initial diagnosis,
therapeutic options are limited and tend to be effective only for small size tumors with
limited spread and minimal vascular invasion. As a result, long-term patient survival
remains minimal, and has not improved in the past three decades. In order to reduce
morbidity and mortality from liver cancer, improvement in early diagnosis and the
evaluation of current treatments are essential.
This study tested the applicability of the Apriori and FP-Growth association data
mining algorithms to liver cancer patient data, obtained from the British Columbia
Cancer Agency. The data was used to develop association rules which indicate what
combinations of factors are most commonly observed with liver cancer incidence as well
as with increased or decreased rates of mortality.
Ideally, these association rules will be applied in future studies using liver cancer
data extracted from other Electronic Health Record (EHR) systems. The main objective
of making these rules available is to facilitate early detection guidelines for liver cancer
and to evaluate current treatment options. === Graduate === 0566 === 0984 === fabiola@uvic.ca |
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