Human Friendly Associative Classifiers for Early Childhood Caries

Abstract

Early childhood caries (ECC) is a widespread disease that may lead to serious complications and impact the whole society. For these reasons, we look for a predictive model that could be easily applied whenever and wherever necessary, especially in poor environments. As a result, we create human friendly classifiers for ECC that could be utilized in prevention programs. These classifiers are rule-based, with a few rules, easy to use even without computers, and without a loss in predictive performance. For this purpose, we mined association rules and clustered them by their contents. Next, we employed a genetic algorithm to assemble a classifier using dissimilar association rules. The proposed approach was tested on a data set about ECC in the South Backa area (Vojvodina, Serbia). We compared the performance of the resulting classifiers to that of the logistic regression model built around the previously identified risk factors.