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1. Introduction
The prevalence of dental caries in preschool children in Taiwan was 79.3% in 2011. Caries risk assessment in children is a crucial part of the clinical decision-making process that dentists apply on a daily basis to provide appropriate preventive measures. Although previous caries experience may be the most informative criterion and a basis for assessing caries risk [1], from a clinical standpoint, the information comes out too late to be useful in preventing caries, as many irrevocable changes have already occurred [2]. Previous studies have investigated parental factors associated with caries in children [3,4,5,6], focusing on the social determinants of the parents [4] and parenting behaviors and practices [3].
Caries development in preschool children has been associated with the family’s socioeconomic situation and oral health behavior [7]. The association between low health literacy and poor health outcomes is well established [8]. Patients with low oral health literacy (OHL) are at the highest risk for oral diseases and problems [9]. Further, low health literacy may be associated with barriers to accessing care and with oral health behaviors such as seeking preventive care [10].
Lack of time for implementing caries risk assessments in clinical practice is another challenge. Regular oral examinations and biometrics tests with simple kits are easily used tools in dentists’ daily practice. To maintain optimal oral health, the American Dental Association (ADA) recommends regular dental visits, at intervals determined by a dentist [11]. Regular dental exams are an important part of preventive oral health care. During a dental visit, professional tooth cleaning and screening for cavities and gum disease are routine procedures. Parental oral health and beliefs usually represent their own clinical outcome [12], and it is associated with the oral health status of their children [13].
Data mining is an effective and practical statistical method for identifying crucial associations in data obtained from various perspectives. The decision tree (DT) model is a powerful and nonparametric statistical method commonly used in data mining to examine complex data and induce the DT, which is used to make classifications or predictions [14]. In dental research, the DT model has been applied to predict primary and secondary caries risk factors in an adult population [15]. Unlike traditional regression model analysis-which uses multiple factors and longitudinal...