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Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods
M. Štepanovský, A. Ibrová, Z. Buk, J. Velemínská,
Jazyk angličtina Země Irsko
Typ dokumentu srovnávací studie, časopisecké články
NLK
ProQuest Central
od 1997-02-07 do Před 2 měsíci
Nursing & Allied Health Database (ProQuest)
od 1997-02-07 do Před 2 měsíci
Health & Medicine (ProQuest)
od 1997-02-07 do Před 2 měsíci
- MeSH
- data mining MeSH
- dentice trvalá * MeSH
- dítě MeSH
- lidé MeSH
- lineární modely MeSH
- mladiství MeSH
- mladý dospělý MeSH
- neuronové sítě MeSH
- předškolní dítě MeSH
- rentgendiagnostika panoramatická MeSH
- rozhodovací stromy MeSH
- software MeSH
- support vector machine MeSH
- určení zubního věku metody MeSH
- zuby růst a vývoj MeSH
- Check Tag
- dítě MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- předškolní dítě MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
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.
Citace poskytuje Crossref.org
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- $a Štepanovský, Michal $u Department of Computer Systems, Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, 160 00 Prague, Czech Republic.
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- $a Novel age estimation model based on development of permanent teeth compared with classical approach and other modern data mining methods / $c M. Štepanovský, A. Ibrová, Z. Buk, J. Velemínská,
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- $a 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.
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- $a Ibrová, Alexandra $u Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43 Prague, Czech Republic. Electronic address: ibrovaa@natur.cuni.cz.
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- $a Velemínská, Jana $u Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Viničná 7, 128 43 Prague, Czech Republic.
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