基于机器学习的四川省 12 岁儿童龋齿预测模型。

Xinmiao Yan, Taolan Sun, Yuhang Lu, Xin Tan, Zhuo Wang, Miaojing Li
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引用次数: 0

摘要

目的采用机器学习算法构建儿童龋齿预测模型,以确定儿童龋齿的风险因素,并提出改善儿童口腔健康的针对性措施和政策建议:方法:本研究采用分层整群随机抽样法。根据四川省不同的政策和措施,随机抽取四川省 8 个城市 3-4 所中学的 12 岁学生进行问卷调查、口腔检查和体格检查。对 12 岁儿童龋齿风险因素进行多元逻辑回归分析。数据集按 7∶3 的比例随机分为训练集和验证集。使用 R 4.1.1 版本构建了随机森林、决策树、极端梯度提升(XGBoost)和逻辑回归等四种机器学习算法,并使用接收者工作特征曲线下面积(AUC)评估了四种预测模型的预测效果:本研究共纳入 4 439 名 12 岁儿童。恒牙龋齿发生率为 50.93%。多变量逻辑回归分析结果显示,体重指数、父亲的最高教育背景、母亲的最高教育背景、是否刷牙、每天刷牙次数、刷牙时使用牙膏、刷牙时间、饭后漱口、刷牙后睡觉前进食、甜饮料、零食、去牙科诊所检查牙齿、刷牙年龄是影响儿童龋齿的因素:在随机森林的基础上建立了儿童龋齿预测模型,显示出良好的预测效果。针对影响儿童龋齿发生的主要因素采取预防措施是有益的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction model of dental caries in 12-year-old children in Sichuan Province based on machine learning.

Objectives: The machine learning algorithm was used to construct a prediction model of children's dental caries to determine the risk factors of dental caries in children and put forward targeted measures and policy suggestions to improve children's oral health.

Methods: Stratified cluster random sampling was adopted in this study. In accordance with different policies and measures in Sichuan Province, 12-year-old students from 3-4 middle schools in eight cities of Sichuan Province were randomly selected for questionnaire survey, oral examination, and physical examination. Multivariate logistic regression analysis of risk factors for dental caries in 12-year-old children was conducted. The dataset was randomly divided into training set and validation set at a ratio of 7∶3. Four machine learning algorithms, including random forest, decision tree, extreme gradient boosting (XGBoost), and Logistic regression, were constructed using R version 4.1.1, and the prediction effects of the four prediction models were evaluated using the area under receiver operating characteristic curve (AUC).

Results: A total of 4 439 children aged 12 years were included in this study. The incidence of permanent teeth caries was 50.93%. The results of multivariate logistic regression analysis showed that body mass index, highest educational background of the father, highest educational background of the mother, whether to brush teeth, how many times a day, use of toothpaste when brushing teeth, duration of brushing teeth, mouthwash after meals, eating before going to bed after brushing teeth, sweet drinks, snacks, going to dental clinic to examine teeth, and age of brushing teeth were the factors influencing children's dental caries (P<0.05). The AUC values predicted by random forest, decision tree, Logistic regression, and XGBoost were 0.840, 0.755, 0.799, and 0.794, respectively. In the random forest model, the variable with the highest contribution was eating before bed after brushing.

Conclusions: A prediction model of dental caries in children was established on the basis of random forest, showing good prediction effect. Taking preventive measures for the main factors affecting the occurrence of dental caries in children is beneficial.

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