利用机器学习方法预测正畸治疗时间

M. H. Elnagar, Allen Y. Pan, Aryo Handono, F. Sanchez, Sameh Talaat, C. Bourauel, Ahmed Kaboudan, B. Kusnoto
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引用次数: 1

摘要

治疗时间是患者决定是否接受正畸治疗的重要因素之一。本研究旨在建立和比较预测正畸治疗时间的机器学习(ML)模型,并利用ML方法确定影响正畸治疗时间的因素。本研究使用518例成功完成正畸治疗的患者的记录。70%的患者数据用于训练ML模型,30%的数据用于测试这些模型。我们应用并比较了九种机器学习算法:简单线性回归、修正简单线性回归、多项式线性回归、K近邻、简单决策树、bagging回归、随机森林、梯度增强回归和adaboost回归。然后,我们计算了具有最高性能的ML模型的患者数据特征的重要性。通过bagging回归和adaboost回归ML方法获得了最佳的综合性能。预测治疗时间的最重要特征是年龄、拥挤程度、人工智能病例困难评分、复盖和复咬。在没有患者信息的情况下,几种ML算法在预测治疗时间方面表现出相当的性能。Bagging和adaboost在提供患者信息(包括年龄、错牙合和拥挤程度)时表现最佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilization of Machine Learning Methods for Predicting Orthodontic Treatment Length
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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