Gum Disease Identification Using Fuzzy Expert System

Abstract

Gum disease, including Gingivitis and Periodontitis, is among the most common dental conditions, primarily caused by dental plaque, a bacterial biofilm. These conditions are strongly linked to various systemic illnesses, including cancer, atherosclerosis, hypertension, stroke, and respiratory and cardiovascular conditions like aspiration pneumonia, as well as adverse pregnancy outcomes. Gum inflammation is typically characterized by symptoms such as increased redness, swelling (edema), and a loss of surface texture (stippling; gum fiber attachment). These symptoms are site-specific, meaning that an individual can have both healthy and diseased areas within their mouth. In this research, we developed a fuzzy expert system using MATLAB to identify gum diseases. The system was tested on various cases and produced an output value of 0.133, which successfully identified Gingivitis. This value was derived using a fuzzy logic system that processes input data through predefined rules within the Fuzzy Expert System (FES). The system utilizes several input variables such as the frequency of gum bleeding, the extent of plaque accumulation, the depth of gum recession, and the degree of tooth mobility. The key contribution of this study lies in the integration of fuzzy logic to handle the inherent uncertainties in clinical diagnosis, providing a more nuanced assessment compared to traditional methods. The novelty of this research is the application of a fuzzy expert system in dental diagnostics, offering a promising tool for improving the accuracy and efficiency of gum disease identification in clinical settings. This system has the potential to assist dentists in making more informed decisions, ultimately leading to better patient outcomes.