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Machine Learning Techniques for Prediction of Age: From Eruption Status Of Permanent Teeth In Sri Lankan Children - Softcover

Nawarathna. PhD, Lakshika S.; De Silva. BSc, Harshani H.; Vithanaarachchi. MD, V.S.N.

 
9786200311023: Machine Learning Techniques for Prediction of Age: From Eruption Status Of Permanent Teeth In Sri Lankan Children
Softcover
ISBN 10: 6200311021 ISBN 13: 9786200311023
Verlag: LAP LAMBERT Academic Publishing, 2022

Inhaltsangabe

Prediction of age is a predominant facet in forensic and clinical fields. Forensic odontology is used to predict an age by using permanent teeth resistant to high temperatures and any mass disaster than other parts of the body. Age-related memory loss, memory loss associated with dementia, and the absence of official documents to verify their age are the main reasons people have no knowledge about their age. Therefore, age prediction is used in various situations, such as identification, admission purposes, employment, criminal issues and judicial punishments. The main objective of this study is to predict the age of a child using the eruption status of permanent teeth. This cross-sectional study was conducted on 3321 individuals (1681 males and 1640 females) from 7 provinces and 20 schools in Sri Lanka. Regression tree algorithms in Machine learning, namely Classification and regression trees (CART), gradient boosting (GB) classifier and extreme gradient boost (XGBoost) classifier, were used to make predictions for the age of a child. The study results provide an XGBoost machine learning classifier as the most suitable method for age prediction with higher accuracy.

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Über die Autorin bzw. den Autor

Lakshika S. Nawarathna, PhDDepartment of Statistics & Computer Science, Faculty of ScienceHarshani H. De Silva, BSc Department of Statistics and Computer ScienceFaculty of ScienceV.S.N. Vithanaarachchi, MDDivision of Orthodontics,Faculty of Dental Sciences University of Peradeniya, Sri Lanka.

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Verlag
LAP LAMBERT Academic Publishing
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
6200311021
ISBN 13 
9786200311023
Einband
Taschenbuch
Anzahl der Seiten
80
Kontakt zum Hersteller
Nicht verfügbar
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