TITLE:
Extracting Significant Patterns for Oral Cancer Detection Using Apriori Algorithm
AUTHORS:
Neha Sharma, Hari Om
KEYWORDS:
Data Mining; Association Rule Mining; Apriori; Oral Cancer; WEKA
JOURNAL NAME:
Intelligent Information Management,
Vol.6 No.2,
March
17,
2014
ABSTRACT: Presently, no effective tool exists for early
diagnosis and treatment of oral cancer. Here, we describe an approach for
cancer detection and prevention based on analysis using association rule
mining. The data analyzed are pertaining to clinical symptoms, history of
addiction, co-morbid condition and survivability of the cancer patients. The
extracted rules are useful in taking clinical judgments and making right
decisions related to the disease. The results shown here are promising and show
the potential use of this approach toward eventual development of diagnostic
assay and treatment with sufficient support and confidence suitable for
detection of early-stage oral cancer.