Fuzzy association rule frequent itemset selection to predict ovarian cancer

Dwina Kuswardani, Aniati Murni Arymurthy

Research output: Contribution to journalArticle

Abstract

Ovarian cancer spread found in the uterus for the reason required condition uterus. In this study, will be conducted pattern the condition of the uterus uses Fuzzy Association Rule. We proposed Fuzzy Association Rule based on frequent itemsets selection (FARFIS) modified Apriori algorithm. The frequent itemsets selection is done based on the selection of items with highest occurrence, after searching 1-itemset on process mining and then process searching 2-itemsets and so on like Apriori algorithm. Experiment is performed on Ovarian Cancer CT scan data. The proposed of method will be compared with fuzzy association rule. Our experimental results showed that computing time is faster than the fuzzy association rule method. The results of classification used rule FARFIS and FAR have same accuracy. The results of the study show that it will help the doctor perform the diagnosis of metastasis of ovarian cancer .

Original languageEnglish
Pages (from-to)453-458
Number of pages6
JournalInformation (Japan)
Volume20
Issue number1
Publication statusPublished - 1 Jan 2017

Keywords

  • Apriori algorithm
  • Association rule
  • Fuzzy association rule
  • Ovarian cancer

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