Identifying Association Rules among Drugs in Prescription of a Single Drugstore Using Apriori Method

Ahmad Yoosofan¹, Fatemeh Ghovanlooy Ghajar², Sima Ayat¹, Somayeh Hamidi¹, Farshad Mahini³

¹Electrical and Computer Engineering Department, University of Kashan, Kashan, Iran.
²Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran.
³Department of Mechanical Engineering, Villanova University, Villanova, PA, USA.

DOI: 10.4236/iim.2015.75020                

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.

Keywords

Data Mining, Association Rules, Purchase Portfolio Analysis, Apriori

Conflicts of Interest

The authors declare no conflicts of interest.

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