Mining of association rules for treatment of dental diseases
Shankar Chakraborty, Bivash Mallick, Santonab Chakraborty
Abstract
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 made to develop related association rules for understanding the roles of different types of medicines prescribed for the treatment of dental diseases, especially tooth pain (odontalgia/dentalgia) and swelling of the tooth (pericoronitis). Seventy-five patient cases from a dentist are analyzed to determine the average number of different types of medicines prescribed, the average number of medicines, and the average cost of treatment, and to mine the corresponding association rules. It is observed from the 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 of 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.