Predictors of tooth loss: A machine learning approach
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Autores
Elani, Hawazin W.
W. Murray Thomson
Kawachi, Ichiro
Chiavegatto Filho, Alexandre D. P.
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Citações na Scopus
Tipo de documento
Artigo Científico
Data
2021
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Resumo
Introduction
Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.
Methods
We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values.
Results
The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone.
Conclusions
Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.
Titulo de periódico
PLoS ONE
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Membros da banca
Área do Conhecimento CNPQ
CIENCIAS DA SAUDE::ODONTOLOGIA
CIENCIAS DA SAUDE::SAUDE COLETIVA
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
ENGENHARIAS::ENGENHARIA BIOMEDICA
CIENCIAS DA SAUDE::SAUDE COLETIVA
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
ENGENHARIAS::ENGENHARIA BIOMEDICA