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Decision Trees as Readable Models for Early Childhood Caries

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Intelligent Decision Technologies 2016

Part of the book series: Smart Innovation, Systems and Technologies ((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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Chen, Y.-L., Hung, L.T.-H.: Using decision trees to summarize associative classification rules. Expert Syst. Appl. 36, 2338–2351 (2009)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  11. Gansky, S.A.: Dental data mining: potential pitfalls and practical issues. Adv. Dent. Res. 17, 109–114 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont CA (1984)

    MATH  Google Scholar 

  17. Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer (2013)

    Google Scholar 

  18. Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 29(2), 119–127 (1980)

    Google Scholar 

  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. 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. 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. 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. RapidMiner Studio—RapidMiner. https://rapidminer.com/products/studio/

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  27. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)

    Article  Google Scholar 

  28. Graphviz—Graph Visualization Software. http://graphviz.org/

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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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Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

    Vladimir Ivančević, Nemanja Igić, Branko Terzić, Marko Knežević & Ivan Luković

Authors
  1. Vladimir Ivančević
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  2. Nemanja Igić
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  3. Branko Terzić
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  4. Marko Knežević
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  5. Ivan Luković
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Corresponding author

Correspondence to Vladimir Ivančević .

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Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, UK

    Robert J. Howlett

  4. University of Canberra, Canberra, Australia

    Lakhmi C. Jain

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Ivančević, V., Igić, N., Terzić, B., Knežević, M., Luković, I. (2016). Decision Trees as Readable Models for Early Childhood Caries. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-39627-9_39

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  • DOI: https://doi.org/10.1007/978-3-319-39627-9_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39626-2

  • Online ISBN: 978-3-319-39627-9

  • eBook Packages: EngineeringEngineering (R0)

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