Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods
| Autor: | Alexandra Ibrová, Zdeněk Buk, Jana Velemínská, Michal Štepanovský |
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| Rok vydání: | 2017 |
| Předmět: |
Male
Support Vector Machine Adolescent Computer science Sample (statistics) computer.software_genre 01 natural sciences Pathology and Forensic Medicine Young Adult 03 medical and health sciences 0302 clinical medicine Polynomial kernel Radiography Panoramic Linear regression Statistics Data Mining Humans 030216 legal & forensic medicine Child Estimation Artificial neural network Decision Trees 010401 analytical chemistry Missing data 0104 chemical sciences Dentition Permanent Support vector machine Child Preschool Linear Models Female Neural Networks Computer Data mining Age Determination by Teeth Tooth Law computer Software Decision tree model |
| Zdroj: | Forensic Science International. 279:72-82 |
| ISSN: | 0379-0738 |
| DOI: | 10.1016/j.forsciint.2017.08.005 |
| Popis: | In order to analyze and improve the dental age estimation in children and adolescents for forensic purposes, 22 age estimation methods were compared to a sample of 976 orthopantomographs (662 males, 314 females) of healthy Czech children and adolescents aged between 2.7 and 20.5 years. All methods are compared in terms of the accuracy and complexity and are based on various data mining methods or on simple mathematical operations. The winning method is presented in detail. The comparison showed that only three methods provide the best accuracy while remaining user-friendly. These methods were used to build a tabular multiple linear regression model, an M5P tree model and support vector machine model with first-order polynomial kernel. All of them have mean absolute error (MAE) under 0.7 years for both males and females. The other well-performing data mining methods (RBF neural network, K-nearest neighbors, Kstar, etc.) have similar or slightly better accuracy, but they are not user-friendly as they require computing equipment and the implementation as computer program. The lowest estimation accuracy provides the traditional model based on age averages (MAE under 0.96 years). Different relevancy of various teeth for the age estimation was found. This finding also explains the lowest accuracy of the traditional averages-based model. In this paper, a technique for missing data replacement for the cases with missing teeth is presented in detail as well as the constrained tabular multiple regression model. Also, we provide free age prediction software based on this wining model. |
| Databáze: | OpenAIRE |
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