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Open AccessArticle

Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules

1
Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
2
Department of Mathematics, Faculty of Mathematics, Computation and Data Science, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
3
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan
4
Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei 11101, Taiwan
5
College of Medicine, National Taiwan University, Taipei 10051, Taiwan
6
College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
7
Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
8
Division of Plastic Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan
9
Cochrane Taiwan, Taipei Medical University, Taipei 11031, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(22), 4962; https://doi.org/10.3390/app9224962
Received: 21 October 2019 / Revised: 7 November 2019 / Accepted: 8 November 2019 / Published: 18 November 2019
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 affects 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. View Full-Text
Keywords: production rule system; non-redundant association rules; rule-based system; knowledge-based systems; non-anomalous rules production rule system; non-redundant association rules; rule-based system; knowledge-based systems; non-anomalous rules
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MDPI and ACS Style

Ou-Yang, C.; Wulandari, C.P.; Iqbal, M.; Wang, H.-C.; Chen, C. Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules. Appl. Sci. 2019, 9, 4962.

AMA Style

Ou-Yang C, Wulandari CP, Iqbal M, Wang H-C, Chen C. Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules. Applied Sciences. 2019; 9(22):4962.

Chicago/Turabian Style

Ou-Yang, Chao; Wulandari, Chandrawati P.; Iqbal, Mohammad; Wang, Han-Cheng; Chen, Chiehfeng. 2019. "Extracting Production Rules for Cerebrovascular Examination Dataset through Mining of Non-Anomalous Association Rules" Appl. Sci. 9, no. 22: 4962.

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