A Comparative Study on Association Rule Mining Algorithms on the Hospital Infection Control Dataset
DOI:
https://doi.org/10.21123/bsj.2023.7571Keywords:
Machine learning, Apriori Algorithm, apriori mlxtend, Hospital Readmission, Association Rule Mining, Performance of Algorithms.Abstract
Administrative procedures in various organizations produce numerous crucial records and data. These
records and data are also used in other processes like customer relationship management and accounting
operations.It is incredibly challenging to use and extract valuable and meaningful information from these data
and records because they are frequently enormous and continuously growing in size and complexity.Data
mining is the act of sorting through large data sets to find patterns and relationships that might aid in the data
analysis process of resolving business issues. Using data mining techniques, enterprises can forecast future
trends and make better business decisions.The Apriori algorithm has been introduced to calculate the
association rules between objects; the primary goal of this algorithm is to establish an association rule between
various things. The association rule describes how two or more objects are related.We have employed the
Apriori property and Apriori Mlxtend algorithms in this study and we applied them on the hospital database;
and, by using python coding, the results showed that the performance of Apriori Mlxtend was faster, and it
was 0.38622, and the Apriori property algorithm was 0.090909. That means the Apriori Mlxtend was better
than the Apriori property algorithm.
Received 19/6/2022
Revised 25/12/2022
Accepted 26/12/2022
Published Online First 20/3/2023
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