Application of machine learning to predict periodontal disease in US adults: A cross-sectional analysis of NHANES 2009–2014

Background: Periodontal disease (PD) is a primary contributor to tooth loss, which negatively affects oral functionality and quality of life. This research aims to investigate the effectiveness of various machine learning (ML) classifiers in identifying PD among U.S. adults. Method: Nineteen features, selected based on prior literature and expert dentist input, were preprocessed using feature engineering techniques. Eleven machine learning classifiers, including basic and ensemble models, were evaluated to identify the best performing model. The interpretability of the model was evaluated using Shapley additive explanations and individual conditional expectation plots to determine key predictors of periodontitis. Results: The predictive efficacy of the ML classifiers is assessed using metrics such as the area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. The CatBoost classifier performed best in identifying PD. It achieved an AUC of 84.5%, an accuracy of 75.8%, a precision of 75.8%, a sensitivity of 78.8%, and a specificity of 72.5%. Having an annual dentist visit and age emerged as the most influential variables. Conclusions: The ML models utilized in this study exhibited robust predictive performance and can be further improved by incorporating additional clinical parameters. The proposed models effectively identified individuals at high risk for developing PD.