Utilization of Machine Learning Methods for Predicting Orthodontic Treatment Length

Abstract

Treatment duration is one of the most important factors that patients consider when deciding whether to undergo orthodontic treatment or not. This study aimed to build and compare machine learning (ML) models for the prediction of orthodontic treatment length and to identify factors affecting the duration of orthodontic treatment using the ML approach. Records of 518 patients who had successfully finished orthodontic treatment were used in this study. Seventy percent of the patient data were used for training ML models, and thirty percent of the data were used for testing these models. We applied and compared nine machine-learning algorithms: simple linear regression, modified simple linear regression, polynomial linear regression, K nearest neighbor, simple decision tree, bagging regressor, random forest, gradient boosting regression, and adaboost regression. We then calculated the importance of patient data features for the ML models with the highest performance. The best overall performance was obtained through the bagging regressor and adaboost regression ML methods. The most important features in predicting treatment length were age, crowding, artificial intelligence case difficulty score, overjet, and overbite. Without patient information, several ML algorithms showed comparable performance for predicting treatment length. Bagging and adaboost showed the best performance when patient information, including age, malocclusion, and crowding, was provided.

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