طراحی یک سیستم تصمیم گیرنده جهت درمان پوسیدگی دندان در کودکان

 Introduction: Dentists have to choose a precise treatment plan based on the prevailing sign symptoms gathered from patients. However; in most of cases, the symptoms are complicate which makes the lack of confidence for the dentist to find an accurate treatment plan. This study introduces a new diagnosis system that helps the dentists and students to choose an accurate course of treatment for dental caries. This diagnostic system is based on Bayesian Network (BN) analysis. Methods: In this system, patient’s symptoms were as input variables and treatments were as output variables. A Bayesian Network is designed for 13 different sign-symptoms and 5 related treatments. K-means clustering algorithm is used to determine the relationships between variables, including symptoms and treatment. Results: The system evaluated by using actual scenario to determine the accuracy and showed reliable outcome. Conclusion: This system can be used in dental schools to teach students.