Decision Trees as Readable Models for Early Childhood Caries

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


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.


Early 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.


  1. 1.
    Ghazal, T., Levy, S.M., Childers, N.K., Broffitt, B., Cutter, G.R., Wiener, H.W., Kempf, M.C., Warren, J., Cavanaugh, J.E.: Factors associated with early childhood caries incidence among high caries-risk children. Commun. Dent. Oral Epidemiol. 43(4), 366–374 (2015)CrossRefGoogle Scholar
  2. 2.
    Corrêa-Faria, P., Martins-Júnior, P.A., Vieira-Andrade, R.G., Marques, L.S., Ramos-Jorge, M.L.: Factors associated with the development of early childhood caries among Brazilian preschoolers. Braz. Oral Res. 27(4), 356–362 (2013)CrossRefGoogle Scholar
  3. 3.
    Tušek, I., Carević, M., Tušek, J.: Prevalence of early childhood caries among members of different ethnic groups in the South Bačka area (in Serbian). Vojnosanit. Pregl. 69(12), 1046–1051 (2012)CrossRefGoogle Scholar
  4. 4.
    Garcia, R., Borrelli, B., Dhar, V., Douglass, J., Ramos Gomez, F., Hieftje, K., Horowitz, A., Li, Y., Ng, M.W., Twetman, S., Tinanoff, N.: Progress in early childhood caries and opportunities in research, policy, and clinical management. Pediatr. Dent. 37(3), 294–299 (2015)Google Scholar
  5. 5.
    Berkowitz, R.J.: Causes, treatment and prevention of early childhood caries: a microbiologic perspective. J. Can. Dent. Assoc. 69(5), 304–307b (2013)Google Scholar
  6. 6.
    Ivančević, V., Tušek, I., Tušek, J., Knežević, M., Elheshk, S., Luković, I.: Using association rule mining to identify risk factors for early childhood caries. Comput. Methods Programs Biomed. 122, 175–181 (2015)CrossRefGoogle Scholar
  7. 7.
    Ivančević, V., Knežević, M., Tušek, I., Tušek, J., Luković, I.: Human friendly associative classifiers for early childhood caries. In: 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015), pp. 243–253. Springer (2015)Google Scholar
  8. 8.
    Chen, Y.-L., Hung, L.T.-H.: Using decision trees to summarize associative classification rules. Expert Syst. Appl. 36, 2338–2351 (2009)CrossRefGoogle Scholar
  9. 9.
    Tušek, I.: The Influence of social environment and ethnicity on caries prevalence in the early childhood (in Serbian). Ph.D. thesis, University of Belgrade (2009)Google Scholar
  10. 10.
    Stewart, P.W., Stamm, J.W.: Classification tree prediction models for dental caries from clinical, microbiological, and interview data. J. Dent. Res. 70(9), 1239–1251 (1991)CrossRefGoogle Scholar
  11. 11.
    Gansky, S.A.: Dental data mining: potential pitfalls and practical issues. Adv. Dent. Res. 17, 109–114 (2003)CrossRefGoogle Scholar
  12. 12.
    Tamaki, Y., Nomura, Y., Katsumura, S., Okada, A., Yamada, H., Tsuge, S., Kadoma, Y., Hanada, N.: Construction of a dental caries prediction model by data mining. J. Oral Sci. 51, 61–68 (2009)CrossRefGoogle Scholar
  13. 13.
    Ito, A., Hayashi, M., Hamasaki, T., Ebisu, S.: Risk assessment of dental caries by using classification and regression trees. J. Dent. 39, 457–463 (2011)CrossRefGoogle Scholar
  14. 14.
    MacRitchie, H.M.B., Longbottom, C., Robertson, M., Nugent, Z., Chan, K., Radford, J.R., Pitts, N.B.: Development of the Dundee Caries Risk Assessment Model (DCRAM)—risk model development using a novel application of CHAID analysis. Commun. Dent. Oral Epidemiol. 40, 37–45 (2012)CrossRefGoogle Scholar
  15. 15.
    Li, H.F.: Data mining and pattern discovery using exploratory and visualization methods for large multidimensional datasets. Ph.D. thesis, University of Kentucky (2013)Google Scholar
  16. 16.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont CA (1984)zbMATHGoogle Scholar
  17. 17.
    Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer (2013)Google Scholar
  18. 18.
    Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 29(2), 119–127 (1980)CrossRefGoogle Scholar
  19. 19.
    Säuberlich, F., Gaul, W.: Decision tree construction by association rules. In: 23rd Annual Conference of the Gesellschaft für Klassifikation, pp. 245–253. Springer (2000)Google Scholar
  20. 20.
    Wang, K., Zhou, S., He, Y.: Growing decision trees on support-less association rules. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’00), pp. 265–269. ACM (2000)Google Scholar
  21. 21.
    Abdekhalim, A., Traore, I., Sayed, B.: RBDT-1: a new rule-based decision tree generation technique. In: International Symposium on Rule Interchange and Applications (RuleML 2009), pp. 108–121. Springer (2009)Google Scholar
  22. 22.
    Peng, Y., Ye, Y., Yin, J.: Decision tree construction algorithm based on association rules. In: 2nd International Conference on Computer Application and System Modeling (ICCASM 2012), pp. 754–756. Atlantis Press (2012)Google Scholar
  23. 23.
    RapidMiner Studio—RapidMiner.
  24. 24.
    Miller, G.A.: The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81–97 (1956)CrossRefGoogle Scholar
  25. 25.
    Fontana, M.: The clinical, environmental, and behavioral factors that foster early childhood caries: evidence for caries risk assessment. Pediatr. Dent. 37(3), 217–225 (2015)Google Scholar
  26. 26.
    Allouche, O., Tsoar, A., Kadmon, R.: Assessing the accuracy of species distribution models: prevalence, kappa and the True Skill Statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006)CrossRefGoogle Scholar
  27. 27.
    Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)CrossRefGoogle Scholar
  28. 28.
    Graphviz—Graph Visualization Software.

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Authors and Affiliations

  • Vladimir Ivančević
    • 1
    Email author
  • 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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