Mathematics (Oct 2021)

Association Rules Mining for Hospital Readmission: A Case Study

  • Nor Hamizah Miswan,
  • ‘Ismat Mohd Sulaiman,
  • Chee Seng Chan,
  • Chong Guan Ng

DOI
https://doi.org/10.3390/math9212706
Journal volume & issue
Vol. 9, no. 21
p. 2706

Abstract

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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.

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