Department of Preventive and Community Dentistry, The University of Iowa College of Dentistry, Iowa City, Iowa, USA.
Division of Biostatistics and Computational Biology, The University of Iowa College of Dentistry, Iowa City, Iowa, USA.
Spec Care Dentist. 2024 Jan-Feb;44(1):148-156. doi: 10.1111/scd.12832. Epub 2023 Feb 7.
Edentulism is an incapacitating condition, and its prevalence is unequal among different population groups in the United States (US) despite its declining prevalence. This study aimed to investigate the current prevalence, apply Machine Learning (ML) Algorithms to investigate factors associated with complete tooth loss among older US adults, and compare the performance of the models.
The cross-sectional 2020 Behavioral Risk Factor Surveillance System (BRFSS) data was used to evaluate the prevalence and factors associated with edentulism. ML models were developed to identify factors associated with edentulism utilizing seven ML algorithms. The performance of these models was compared using the area under the receiver operating characteristic curve (AUC).
An overall prevalence of 11.9% was reported. The AdaBoost algorithm (AUC = 84.9%) showed the best performance. Analysis showed that the last dental visit, educational attainment, smoking, difficulty walking, and general health status were among the top factors associated with complete edentulism.
Findings from our study support the declining prevalence of complete edentulism in older adults in the US and show that it is possible to develop a high-performing ML model to investigate the most important factors associated with edentulism using nationally representative data.
无牙颌是一种使人丧失能力的状况，尽管其患病率在下降，但在美国（US）不同人群中的分布并不均衡。本研究旨在调查当前的患病率，应用机器学习（ML）算法调查与美国老年人完全失牙相关的因素，并比较模型的性能。
使用 2020 年行为风险因素监测系统（BRFSS）的横断面数据评估无牙颌的患病率和相关因素。利用七种 ML 算法开发了 ML 模型，以确定与无牙颌相关的因素。使用接收者操作特征曲线下的面积（AUC）比较这些模型的性能。
报告的总体患病率为 11.9%。AdaBoost 算法（AUC=84.9%）表现出最佳性能。分析表明，最近一次看牙医、教育程度、吸烟、行走困难和总体健康状况是与完全无牙颌相关的最重要因素。
我们的研究结果支持美国老年人完全无牙颌患病率的下降，并表明使用全国代表性数据开发高性能 ML 模型来调查与无牙颌相关的最重要因素是可行的。
