Machine Learning‐Based Prediction Model for Alveolar Bone Defect Risk Following Orthodontic Treatment

This study successfully developed an XGBoost machine learning model incorporating 20 variables to predict the risk grade of alveolar bone defects following orthodontic treatment. The model demonstrated excellent predictive performance, significantly outperforming conventional assessment methods and other commonly used machine learning models. Core variables such as the pre score, lower posterior length, and upper mid base width were found to substantially enhance its predictive capability. This model shows potential as a clinical decision-support tool to assist orthodontists in pre-treatment risk assessment and personalised treatment planning.