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Association Rules Mining for Hospital Readmission: A Case Study

Author

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  • Nor Hamizah Miswan

    (Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia
    Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • ‘Ismat Mohd Sulaiman

    (Health Informatics Centre, Planning Division, Ministry of Health Malaysia, Putrajaya 62590, Malaysia)

  • Chee Seng Chan

    (Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Chong Guan Ng

    (Department of Psychological Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

As an indicator of healthcare quality and performance, hospital readmission incurs major costs for healthcare systems worldwide. Understanding the relationships between readmission factors, such as input features and readmission length, is challenging following intricate hospital readmission procedures. This study discovered the significant correlation between potential readmission factors (threshold of various settings for readmission length) and basic demographic variables. Association rule mining (ARM), particularly the Apriori algorithm, was utilised to extract the hidden input variable patterns and relationships among admitted patients by generating supervised learning rules. The mined rules were categorised into two outcomes to comprehend readmission data; (i) the rules associated with various readmission length and (ii) several expert-validated variables related to basic demographics (gender, race, and age group). The extracted rules proved useful to facilitate decision-making and resource preparation to minimise patient readmission.

Suggested Citation

  • Nor Hamizah Miswan & ‘Ismat Mohd Sulaiman & Chee Seng Chan & Chong Guan Ng, 2021. "Association Rules Mining for Hospital Readmission: A Case Study," Mathematics, MDPI, vol. 9(21), pages 1-21, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2706-:d:664131
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    References listed on IDEAS

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    1. So Hyun Park & Shin Yi Jang & Ho Kim & Seung Wook Lee, 2014. "An Association Rule Mining-Based Framework for Understanding Lifestyle Risk Behaviors," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
    2. Snigdha Jain & Rohan Khera & Eric M Mortensen & Jonathan C Weissler, 2018. "Readmissions of adults within three age groups following hospitalization for pneumonia: Analysis from the Nationwide Readmissions Database," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-12, September.
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    Cited by:

    1. Xiaoji Wan & Fen Chen & Hailin Li & Weibin Lin, 2022. "Potentially Related Commodity Discovery Based on Link Prediction," Mathematics, MDPI, vol. 10(19), pages 1-27, October.

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