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RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

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This article was retracted on 12 December 2022

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Abstract

Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient’s medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created. In order to overcome this issue, an enhanced Apriori algorithm (EAA) based on the knowledge of a context ontology (EAA-SMO) methodology for sequential minimal optimization (SMO) is suggested. The simple knowledge is to establish the ideas of ontology as a hierarchical structure of the conceptual clusters of specific subjects, which comprises “similar” concepts that mean an exact category of the knowledge within the domain. There is an interesting rule for each cluster based on the correlation between the items. In addition, the rule developed is classified as a prediction model for anomaly detection based on SMO regression. The experimental analysis demonstrates the proposed method improved 2% of accuracy and minimizes the execution time by 25% when compared to semantic ontology.

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Abbreviations

Cl:

Candidate list

TID:

Transaction ID

FI:

Frequent itemset

χ :

Region of input

\( \varepsilon \) :

Maximum error

F :

Linear function

\( \xi_{i} \) :

Slack variable

x min :

Minimum value

x max :

Maximum value

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Acknowledgements

The authors would like to express their very great appreciation to Reviewers for valuable suggestions, and willingness to give your time so generously has been very much appreciated.

Author information

Authors and Affiliations

  1. Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, 626126, India

    M. Sornalakshmi & S. Balamurali

  2. Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, 626126, India

    M. Sornalakshmi & S. Balamurali

  3. St. Joseph College of Engineering, Sriperumbudur, Chennai, Tamilnadu, 602117, India

    M. Venkatesulu & M. Navaneetha Krishnan

  4. Hindusthan College of Engineering and Technology, Coimbatore, India

    Lakshmana Kumar Ramasamy

  5. Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon

    Seifedine Kadry

  6. University of California, Davis, USA

    Gunasekaran Manogaran

  7. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Ching-Hsien Hsu

  8. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Ching-Hsien Hsu

  9. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan

    Ching-Hsien Hsu

  10. V.R.S. College of Engineering and Technology, Viluppuram, India

    Bala Anand Muthu

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  1. M. Sornalakshmi
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  2. S. Balamurali
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  4. M. Navaneetha Krishnan
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  5. Lakshmana Kumar Ramasamy
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  6. Seifedine Kadry
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  7. Gunasekaran Manogaran
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  8. Ching-Hsien Hsu
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  9. Bala Anand Muthu
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Correspondence to Seifedine Kadry.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-022-08154-9

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Sornalakshmi, M., Balamurali, S., Venkatesulu, M. et al. RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry. Neural Comput & Applic 34, 10597–10610 (2022). https://doi.org/10.1007/s00521-020-04862-2

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