Predictors of tooth loss: A machine learning approach.
PLoS One
; 16(6): e0252873, 2021.
Article
in En
| MEDLINE
| ID: mdl-34143814
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Tooth Loss
Type of study:
Prognostic_studies
/
Risk_factors_studies
Aspects:
Equity_inequality
/
Patient_preference
Limits:
Adult
/
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Journal:
PLoS One
Journal subject:
CIENCIA
/
MEDICINA
Year:
2021
Document type:
Article
Affiliation country: