Identifying early permanent teeth caries factors in children using random forest algorithm - Masaebi, Fatemeh; Ghorbani, Zahra; Azizmohammad Looha, Mehdi; Deghatipour, Marzie; Mohammadzadeh, Morteza; Ahsaie, Mitra Ghazizadeh; Asadi, Fariba; Zayeri, Farid (Frontiers in Dental Medicine, 2024)
Early permanent dental caries can pose a serious threat to oral health in the coming years. This study aimed to investigate the key factors influencing early dental caries in permanent teeth among first-grade Iranian children.A cross-sectional study involving 778 randomly selected first-grade children from public schools in Tehran, Iran, was conducted between November 2017 and January 2018. The oral health of the children, evaluated by two trained dentists, was recorded based on the DMFT index. Information on maternal education, gender, dmft index, brushing frequency, dental visits, flossing, and sweet consumption was also collected. The Random Forest method was employed to identify factors associated with early permanent dental caries, and its performance was compared with logistic regression using the Area Under the Curve (AUC) index Results: Logistic regression, represented by odds ratios (OR), revealed a significant association between early permanent dental caries and dmft index (OR1.13, 95%CI (1.07, 1.20), p-value<0.001), maternal education (OR=2.04, 95%CI (1.15, 3.62), p-value<0.05), and sweet consumption (OR=0.59, 95%CI (0.36, 0.98), p-value<0.05). Random Forest analysis indicated that male gender, higher maternal education, and lower sweet consumption were associated with increased likelihood of being caries-free. Notably, Random Forest demonstrated superior performance (AUC=0.81) compared to logistic regression (AUC=0.72).Early permanent dental caries can be effectively managed by caring primary teeth and reducing consumption of sweet. Maternal education emerged as a pivotal factor in mitigating the risk of early permanent dental caries. Therefore, prioritizing these factors and preventing permanent teeth caries in childhood can be remarkably influential in reducing future caries. The usage of the Random Forest algorithm is highly recommended for identifying relevant risk factors associated with early permanent teeth.