Investigation of a Decision Making System for Dental Caries Treatment in Children

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
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
The system evaluated by using actual scenario to determine the accuracy and showed reliable outcome.
This system can be used in dental schools to teach students.