Abstract: Objective: To investigate the influencing factors of temporomandibular disorders (TMD) in young people, and to construct a TMD risk prediction model for young people based on machine learning (ML) methods in order to provide a more accurate and effective risk assessment tool. Methods: A total of 960 eligible college students were included in the study. Univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to screen for risk factors. Six ML methods were used to construct the TMD risk prediction model and the Shapley additive explanations (SHAP) algorithm was used to interpret the final model. Results: A total of 12 predictors were included in the model construction, and the random forest (RF) model performed best among the 6 ML models. The model had an area under the receiver operating characteristic (ROC) curve (AUC) of 0.863 (95%CI: 0.812-0.915), an accuracy of 0.732, a sensitivity of 0.898, a specificity of 0.728, a positive predictive value (PPV) of 0.864, and a negative predictive value (NPV) of 0.703 on the external test set. The calibration curve showed that the predictive performance of the model was basically consistent with the actual situation, and the decision curve analysis (DCA) showed that the model had good clinical applicability. Conclusion: The TMD risk prediction model for young people constructed based on the ML method has good prediction performance and clinical applicability, which can help the clinic to carry out more efficient disease management and more accurate medical interventions.

Key words: temporomandibular disorders, machine learning, prediction model, SHAP algorithm