Mining of association rules for treatment of dental diseases


  • Shankar Chakraborty Industrial Engineering and Management Department, Maulana Abul Kalam Azad University of Technology, West Bengal, India
  • Bivash Mallick Industrial Engineering and Management Department, Maulana Abul Kalam Azad University of Technology, West Bengal, India
  • Santonab Chakraborty Department of Production Engineering, Jadavpur University, Kolkata, India



Data mining; Association rule; Dental disease; Support; Confidence


A prior knowledge regarding the effectiveness of each of the medicines prescribed by a physician would be quite helpful to a patient for rapid recovery from a particular disease. In this paper, an attempt is put forward to develop the related association rules for understanding the roles of different types of medicines prescribed for treatment of dental diseases, especially tooth pain (odontalgia/dentalgia) and swelling of tooth (pericoronitis). 75 patient cases from a dentist are analyzed to determine the average number of different types of medicines prescribed, average number of medicines and average cost of treatment, and to mine the corresponding association rules. It is observed from 1-item dataset that antibiotic#1 is the most preferred medicine, followed by antiseptic. Similarly, the 2-item dataset shows that the most preferred combination on medicines is {antibiotic#1, antiseptic}, followed by {antibiotic#1, anti-reflux}. Among all the association rules developed, the rule (If antibiotic#1 and antibiotic#2 and antiseptic, then anti-reflux) appears with the maximum strength.



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How to Cite

Chakraborty, S., Mallick, B., & Chakraborty, S. (2022). Mining of association rules for treatment of dental diseases. Journal of Decision Analytics and Intelligent Computing, 2(1), 1–11.