Mining of association rules for treatment of dental diseases

Abstract

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.

A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production

“…These suppliers can have a better or worse performance than the experts' estimate, but this uncertainty stems from the lack of complete information that would facilitate a more informed decision [61]. Therefore, in practice, the approach of applying linguistic evaluations is often used for assessing the importance of criteria or evaluating suppliers, as this approach is more aligned with human thinking than numerical ratings [62][63][64]. To determine which suppliers can best assist Oglavina in adapting to market requirements, the fuzzy-rough SWARA and ARAS methods were utilized in this research.…”

Development of the MCDM fuzzy LMAW-grey MARCOS model for selection of a dump truck

“…Stability of the results of the proposed methodology must be checked in one of the following ways (Durmić et al, 2020;Biswas, 2020;Jovčić et al, 2020;Gorcun et al, 2021;Badi & Abdulshahed, 2021;Arvanitis et al, 2021;Biswas et al, 2022;Tešić et al, 2022;Stević, et al, 2022;Đukić et al, 2022;Chakraborty et al, 2022). Results consistency checking is done by analyzing sensitivity with Grey MARCOS method to the weight coefficients change by using 26 different scenarios as shown in the Figure 3.…”