Prediction of the mesiodistal width of unerupted permanent canines and premolars: A statistical approach

Boboc, Alin; Dibbets, J. M. H.

doi:10.1016/j.ajodo.2008.04.031

Cited by 21 publications

(33 citation statements)

References 11 publications

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“…The results obtained by the new multiple linear regression equations are significant better than those provided by some classical statistical approaches [3], [15], [17].…”

Section: Comparative Analysismentioning

confidence: 87%

“…For estimation the size of the unerupted canines and premolars we chose a recently proposed equation [3] In the following is presented our approach in optimization of the regression coefficients presented above, to provide a more accurate method for prediction of the mesiodistal width of unerupted permanent canines and premolars.…”

Section: Subjects Materials and Prediction Methodsmentioning

confidence: 99%

“…A comparison of correct estimations, overestimations and underestimations, provided by the original MLRE equations [3] and respectively by the optimized equations from figure 7, is presented in the Table 5: Maximum errors in estimating the sum of the mesiodistal sizes of unerupted canines and premolars…”

Section: Comparative Analysismentioning

confidence: 99%

“…Our aim was to verify if optimizing through an original method the MLRE used recently in the literature [3], [14], can be predicted with sufficient accuracy the sizes of unerupted teeth from the support area in a group of children in Sibiu located in a central area of Romania.…”

Section: Using the Breeder Ga To Optimize A Multiple Regression Analymentioning

confidence: 99%

“…Methods of estimation, performed during mixed dentition, can be grouped into three categories: those using multiple linear regression equations, those using radiographs and those using a combination of the two methods [1], [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

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Using the Breeder genetic algorithm to optimize a multiple regression analysis model used in prediction of the mesiodistal width of unerupted teeth

Stoica

Boitor

2014

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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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.

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“…The results obtained by the new multiple linear regression equations are significant better than those provided by some classical statistical approaches [3], [15], [17].…”

Section: Comparative Analysismentioning

confidence: 87%

Section: Subjects Materials and Prediction Methodsmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Using the Breeder Ga To Optimize A Multiple Regression Analymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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