Extracting production rules for cerebrovascular examination dataset through mining of non-anomalous association rules

Chao Ou-Yang, Chandrawati Putri Wulandari, Mohammad Iqbal, Han Cheng Wang, Chiehfeng Chen

研究成果: 雜誌貢獻文章同行評審

2 引文 斯高帕斯(Scopus)

摘要

Today, patients generate a massive amount of health records through electronic health records (EHRs). Extracting usable knowledge of patients' pathological conditions or diagnoses is essential for the reasoning process in rule-based systems to support the process of clinical decision making. Association rule mining is capable of discovering hidden interesting knowledge and relations among attributes in datasets, including medical datasets, yet is more likely to produce many anomalous rules (i.e., subsumption and circular redundancy) depends on the predefined threshold, which lead to logical errors and aects the reasoning process of rule-based systems. Therefore, the challenge is to develop a method to extract concise rule bases and improve the coverage of non-anomalous rule bases, i.e., one that not only reduces anomalous rules but also finds the most comprehensive rules from the dataset. In this study, we generated non-anomalous association rules (NAARs) from a cerebrovascular examination dataset through several steps: obtaining a frequent closed itemset, generating association rule bases, subsumption checking, and circularity checking, to fit production rules (PRs) in rule-based systems. Toward the end, the rule inferencing part was performed by PROLOG to obtain possible conclusions toward a specific query given by a user. The experiment shows that compared with the traditional method, the proposed method eliminated a significant number of anomalous rules while improving computational time.

原文英語
文章編號4962
期刊Applied Sciences (Switzerland)
9
發行號22
DOIs
出版狀態已發佈 - 11月 1 2019

ASJC Scopus subject areas

  • 材料科學(全部)
  • 儀器
  • 工程 (全部)
  • 製程化學與技術
  • 電腦科學應用
  • 流體流動和轉移過程

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