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Multivariate features selection from demographic and dietary descriptors as caries risk determinants in oral health diagnosis: Data from NHANES 2013–2014

机译:从人口统计和饮食描述符选择的多变量特征,因为龋齿风险决定因素在口腔健康诊断中:来自Nhanes 2013-2014的数据

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The main pathology that make up the epidemiological profile of oral health status is dental caries. This pathology is considered a challenge because its high prevalence, besides being a chronic but preventable disease, which can be caused by a series of different demographic, dietary and laboratory data, among others. Therefore, in this research two multivariate models were proposed for the classification between control patients (`0'), presence of caries (`1') and presence of restorations (`2'). Analyzing 189 demographic and dietary features from NHANES 2013-2014, by FBS method based on the P-value they were reduced to 24 features, which were separated in two sets according to their demographic or dietary information. Both datasets were subjected to a statistical analysis based on linear regression, obtaining their residuals, AUC, ROC and OR values, in order to validate their classification accuracy. For each model were obtained significant statistical results and none of them presented problems related to outliers, according to their residuals. Both models shown P-values > 0.05 for most features and small OR confidence intervals. Finally, demographic data model obtained an AUC = 0.664 and dietary data model obtained an AUC = 0.633, averaging the AUC values for each outcome, proving to be statistically significant. By these results it's concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status patients, based on their likelihood of developing caries.
机译:构成口腔健康状况流行病学概况的主要病理是龋齿。这种病理学被认为是一个挑战,因为它具有较高的患病率,除了是一种慢性但可预防的疾病,这可能是由一系列不同的人口,饮食和实验室数据等引起的。因此,在本研究中,提出了两个多变量模型,用于对照患者(`0')之间的分类,龋齿(`1')和修复的存在(`2')。通过基于P值的FBS方法分析来自NHANES 2013-2014的189年人口和饮食特征,它们减少到24个特征,根据其人口或饮食信息,分两套分开。基于线性回归的统计分析,基于线性回归,获得其残差,AUC,ROC和或值,以验证其分类准确性。对于每个模型,获得了显着的统计结果,因此,根据其残留物,他们均未呈现与异常值相关的问题。两种模型都显示了p值> 0.05,适用于大多数特征和小或置信区间。最后,获得了人口统计数据模型AUC = 0.664,膳食数据模型获得AUC = 0.633,对每个结果进行平均,证明是统计学意义。通过这些结果,它的结论是,这些特定的人口统计和膳食特征是估计口腔健康状况患者的重要决定因素,根据他们的发展龋齿的可能性。

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