Using the Breeder GA to Optimize a Multiple Regression Analysis Model used in Prediction of the Mesiodistal Width of Unerupted Teeth

For the prediction of the unerupted canine and premolars mesiodistal size, have been proposed different variants of multiple linear regression equations (MLRE). These are based on the amount of the upper and lower permanent incisors with a tooth of the lateral support. The aim of the present study was to develop a method for optimization of MLRE, using a genetic algorithm for determining a set of coefficients that minimizes the prediction error for the sum of permanent premolars and canines dimensions from a group of young people in an area Romania's central city represented by Sibiu. To test the proposed method, we used a multiple linear regression equation derived from the estimation method proposed by Mojers to which we adjusted regression coefficients using the Breeder genetic algorithm proposed by Muhlenbein and Schlierkamp. A total of 92 children were selected with complete permanent teeth which had not clinically visible dental caries, proximal restorations or orthodontic treatment that requires the decrease of the mesiodistal size of teeth. For each of these models was made a hard dental stone which was then measured with a digital calliper, the instrument having an accuracy of 0.01 mm. To improve prediction equations, we divided data into training and validation sets. The Breeder algorithm, using the training set, will provide new values for regression coefficients and error term. The validation set was used to test the accuracy of the new proposed equations.