The described methodology could be useful in ECC research because the resulting patterns include associations of potential risk factors. As ECC is a multifactorial disease, researchers need to combine factors to explain the risk of ECC appearance. Researchers have attempted to expand basic microbiological models for ECC development, and to include various social, demographic and behavioral factors [50] such as ethnicity, family income, maternal education level, family status and parental knowledge, as well as inappropriate nursing habit, since the reason for disease appearance is still unknown. The main strengths of our study when compared to other DM-based studies on ECC [19–21] are: (i) examination of a different set of potential risk factors; (ii) usage of different techniques, namely ARM; and (iii) better readability of the results. Moreover, as opposed to using DM for ECC prediction, which is the main goal of other studies, we focus on ECC description. The used approach may lead to a list of potential risk factors for the analyzed environment. We identified the following ECC-related patterns: (i) male children; (ii) male children whose parents are not well informed about dental health; (iii) children who were frequently breastfed; (iv) third or subsequent child in the family; (v) lack of fluency in Serbian (the official language); and (vi) low child’s body weight at birth. Contrary to the expectations, the relationship between parent health awareness and ECC [30] was significant only in male children. The sometimes-questioned relationship between breastfeeding and ECC [37] seems to hold true when breastfeeding is frequent and coupled with other factors. As discussed in Section 3, the remaining risk factors are con- firmed or recognized in other studies [14,32,43,44]. In our future work, we plan to formally model the domain knowledge about ECC, and use it during rule selection and interpretation. We also intend to experiment with summarization of ARs into a decision tree and check if that would lead to understandable predictive models.