Abstract
The objective of an expert system in the field of medicine is to support doctors during the diagnosis process. Any software that is capable of drawing conclusions and making decisions based on the data stored in its database can be called an "expert system." Expert systems are widely employed in many industries, including the health sector. There are numerous types of dental ailments in the field of dentistry. Few symptoms were employed in the existing methods for dental diagnostics. A diagnosis in dentistry requires more than a few symptoms. The aim of this chapter is to analyse a medical expert system based on fuzzy rules that is used for the diagnosis of dental illnesses. Fuzzy inference based on a possibility metric and information extraction based on fuzzy clustering are both used in the modeling. The relevant parameters for dentistry were determined in the initial modeling phase of the system using clinical data. The evaluation of the dental variables based on soft computing will be carried out in the next stage. The third step introduces applied fuzzy inference based on possibility measures and provides examples to support it. The case data gathered from 100 patients makes up the knowledge foundation of the modeled system. Keywords: dental disease, big data, expert system, fuzzy clustering
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