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Identifying Association Rules among Drugs in Prescription of a Single Drugstore Using Apriori Method
Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran
Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
Department of Mechanical Engineering, Villanova University, Villanova, PA, USA
- 1 Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
- 2 Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran
- 3 Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
- 4 Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran
- 5 Department of Mechanical Engineering, Villanova University, Villanova, PA, USA
Intelligent Information Management·Volume 07 (2015)·Pages 253–259·Published 16 September 2015·DOI10.4236/iim.2015.75020
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Abstract
These days, health care systems such as pharmacies and drugstores normally produce high volumes of data. Consequently, utilizing data mining methods in health care systems has become a conventional process. In this research, Apriori algorithm has been applied to perform data mining using the data obtained from the prescriptions ordered within a pharmacy. Ten association rules were achieved from the assigned pharmaceutical drugs in those prescriptions using the aforementioned Apriori algorithm. The accuracy of these rules is also manually studied and reviewed by a physician. Among these association rules, Vitamin D and Calcium pills are the most interrelated medications, and Omeprazole and Metronidazole rankd second in terms of association. The results of this study provide useful feedback information about associations among drugs.
KeywordsData MiningAssociation RulesPurchase Portfolio AnalysisApriori
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