Predictors of tooth loss: A machine learning approach

• Published in: PloS one Vol. 16; no. 6; p. e0252873
• Main Authors: Elani, Hawazin W., Batista, André F. M., Thomson, W. Murray, Kawachi, Ichiro, Chiavegatto Filho, Alexandre D. P.
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 18.06.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Cardiovascular disease; Computer and Information Sciences; Data mining; Decision trees; Dental care; Dental disorders; Dentistry; Diagnosis; Editing; Epidemiology; Evaluation; Health care access; Health insurance; Health policy; Health risks; Hospitals; Income; Intensive care units; Learning algorithms; Low income groups; Machine learning; Marginalized groups; Medicine; Medicine and Health Sciences; Methodology; Oral hygiene; Physical Sciences; Prevention; Public health; Quality of life; Research and Analysis Methods; Socioeconomic factors; Survival; Teeth; Tooth loss; Variables; Visualization; United States; Brazil; United States > US; Massachusetts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0252873

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. 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. 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. 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.