Machine Learning Techniques for Prediction of Age - Nawarathna. PhD, Lakshika S.;De Silva. BSc, Harshani H.;Vithanaarachchi. MD, V.S.N.
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
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.