A Comparative Study of Machine Learning Techniques for Caries Prediction

There are striking disparities in the prevalence of dental disease by income. Poor children suffer twice as much dental caries as their more affluent peers, but are less likely to receive treatment. This paper presents an experimental study of the application of machine learning methods to the problem of caries prediction. For this paper a data set collected from interviews with children under five years of age, in 2006, in Recife, the capital of Pernambuco, a state in northeast Brazil, was built. Four different data mining techniques were applied to this problem and their results were confronted in terms of the classification error and area under the ROC curve (AUC). Results showed that the MLP neural network classifier out performed the other machine learning methods employed in the experiments, followed by the support vector machine (SVM) predictor. In addition, the results also show that some rules (extracted by decision tress) may be useful for understanding the most important factors that influence the occurrence of caries in children.

1 Introduction

The early childhood caries is a disease that occurs in young kids and is associated with malnutrition and inadequate eating habits during weaning. Dental caries is the single most common chronic childhood disease −5 times more common than asthma and 7 times more common than hay fever. This disease is considered a public health problem due to its impact in quality of life; it affects, almost exclusively, children of social-economic groups less privileged in developed and developing countries. Preceded by enamel defects, the early childhood caries may have limited its progress if detected early [22] [21].