Predictors of tooth loss: A machine learning approach

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.

The effect of denture-wearing on physical activity is associated with cognitive impairment in the elderly: A cross-sectional study based on the CHARLS database

“…Our study supports previous research that dentures can repair the loss of health due to tooth loss ( Chen et al, 2020 ). In fact, socioeconomic status, such as education and income level, is highly correlated with access to dental care, as evidenced by the availability of dentures ( Chalub et al, 2016 ; Elani et al, 2021 ). Older adults with impaired oral function can experience physical weakness and other adverse health outcomes, including death ( Fried et al, 2001 ; Tanaka et al, 2018 ; Hakeem et al, 2021 ).…”

Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis

“…Furthermore, their study was mainly focused on suggesting different validation strategies for tooth loss predictions. Elani et al (2021) used cross-sectional data to assess the performance of multiple machine learning models in predicting tooth loss related outcomes using a variety of socioeconomic, self-reported dental care, and general health related predictors. Although the demographics of our sample population is different from the sample used in Elani et al (adults vs older adults, the United States vs Japan), socioeconomic factors such as income, education, and employment were found to be important predictors of tooth loss in both studies.…”

“…In a recent cross-sectional study, Elani et al (2021) predicted tooth loss among adults (aged 20 years or older) in the United States using machine learning algorithms (Elani et al, 2021).…”

Diagnosis of Tooth Prognosis Using Artificial Intelligence

“…It is also reported that dentists have become dependent on computer applications to gain insights for clinical decision making [ 30 , 31 , 32 ]. Machine-learning algorithms were utilized to identify predictors for tooth loss from the National Health and Nutrition Examination survey and assisted clinicians in prioritizing interventions to prevent tooth loss [ 33 ]. However, to date, little is known about AI implemented in comprehensive dental treatment planning.…”