Web-Based Expert System for Dental Disease Diagnosis Using the Certainty Factor Method: A Case Study at Al-Fatah Primary Clinic

Abstract

Dental health has an important role in supporting overall body health. However, public awareness of the importance of treatment and early detection of dental diseases is still low. This study aims to design and develop a web-based expert system that is able to help early diagnosis of dental diseases using the Certainty Factor (CF) method. The CF method was chosen because it is able to handle uncertainty and produce probability estimates based on the symptoms inputted by the patient. The system was developed using the Waterfall approach and implemented in the Al-Fatah Primary Clinic case study. Evaluation of the system's performance shows that the CF method has an accuracy of 89%, outperforming the Naïve Bayes comparison method which only reaches 82%. The system is also equipped with an interactive interface for patients and administrators, as well as features for managing data on symptoms, diseases, and diagnosis results. The results show that this CF-based expert system is able to provide efficient solutions for early consultation of dental diseases independently and accelerate diagnosis services in clinics.