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Decision Trees as Readable Models for Early Childhood Caries

  • Vladimir Ivančević
  • Nemanja Igić
  • Branko Terzić
  • Marko Knežević
  • Ivan Luković
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

Abstract

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.

Keywords

Early childhood caries Risk assessment Decision tree 

Notes

Acknowledgments

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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© Springer International Publishing Switzerland 2016

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Authors and Affiliations

  • Vladimir Ivančević
    • 1
  • Nemanja Igić
    • 1
  • Branko Terzić
    • 1
  • Marko Knežević
    • 1
  • Ivan Luković
    • 1
  1. 1.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia

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