DENTAL ADVISORY SYSTEM USING DECISION TREE ALGORITHM

Dental diseases such as dental caries, gingivitis, and periodontitis are among the most common chronic health conditions affecting millions of individuals worldwide. Early detection and preventive care are essential to reduce complications, treatment costs, and long-term oral health problems. However, many individuals delay dental consultations due to lack of awareness, accessibility issues, or fear of treatment. This project proposes X-Dent AI: An Explainable Dental Advisory and Early Screening System that leverages machine learning and image analysis to assist users in identifying potential dental problems at an early stage. The system integrates a symptombased diagnostic model and an image-based classification model to provide a preliminary dental assessment. Users can input symptoms such as tooth pain, bleeding gums, sensitivity, swelling, and bad breath through an interactive interface. These inputs are analyzed using machine learning algorithms, particularly Decision Tree and Random Forest classifiers, to predict possible dental conditions and provide a confidence score along with an explanation of the contributing symptoms. Additionally, users can upload dental images that are analyzed using a Convolutional Neural Network based on MobileNetV2 transfer learning to detect visual patterns associated with dental diseases. The backend architecture is developed using FastAPI in Python to ensure scalability and high performance, while MySQL is used for secure data storage. The system includes JWT-based authentication and role-based access control to maintain data security and administrative oversight. Furthermore, the system incorporates a continuous learning mechanism where validated predictions can be added to the training dataset, allowing periodic retraining of models to improve accuracy over time. The proposed solution aims to improve dental awareness, support early screening, and provide accessible digital dental advisory support.