Using association rule mining to identify risk factors for early childhood caries

• We look for an approach to massive risk factor discovery for early childhood caries.
• Association rule mining with adequate rule pruning and ranking performs well.
• Literature supports most risk factors identified in associative analysis.
• When coupled with other risk factors frequent breastfeeding confirmed as a risk.
• Parent health awareness significant only for boys.

Background and objectiveEarly childhood caries (ECC) is a potentially severe disease affecting children all over the world. The available findings are mostly based on a logistic regression model, but data mining, in particular association rule mining, could be used to extract more information from the same data set.MethodsECC data was collected in a cross-sectional analytical study of the 10% sample of preschool children in the South Bačka area (Vojvodina, Serbia). Association rules were extracted from the data by association rule mining. Risk factors were extracted from the highly ranked association rules.ResultsDiscovered dominant risk factors include male gender, frequent breastfeeding (with other risk factors), high birth order, language, and low body weight at birth. Low health awareness of parents was significantly associated to ECC only in male children.ConclusionsThe discovered risk factors are mostly confirmed by the literature, which corroborates the value of the methods.