Decision Trees as Readable Models for Early Childhood Caries
Assessing risk for early childhood caries (ECC) is a relevant task in public health care and an important activity in fulfilling this task is increasing the knowledge about ECC. Discovering important information from data and sharing it in an understandable format with both experts and the general population could be beneficial for advancing and spreading the knowledge about this disease. After having experimented with association rule mining, we investigate the possibility of using decision trees as readable models in risk assessment. We build various decision trees using different algorithms and splitting criteria, favouring compact decision trees with good predictive performance. These decision trees are compared to the previous ECC models for the same analyzed population, namely a logistic regression model and an associative classifier, as well as to decision trees for caries from other studies. The results indicate flexibility and usefulness of decision trees in this context.
KeywordsEarly childhood caries Risk assessment Decision tree
The research presented in this paper was supported by the Ministry of Education, Science, and Technological Development of the Republic of Serbia under Grant III-44010. The authors are most grateful to Ivan Tušek and Jasmina Tušek for the provided data set and valuable support throughout the study.
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