A Comparative Study on Support Vector Machine and Constructive RBF Neural Network for Prediction of Success of Dental Implants

  • Adriano L. I. Oliveira
  • Carolina Baldisserotto
  • Julio Baldisserotto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


The market demand for dental implants is growing at a significant pace. In practice, some dental implants do not succeed. Important questions in this regard concern whether machine learning techniques could be used to predict if an implant will be successful and which are the best techniques for this problem. This paper presents a comparative study on three machine learning techniques for prediction of success of dental implants. The techniques compared here are: (a) support vector machines (SVM); (b) weighted support vector machines; and (c) constructive RBF neural networks (RBF-DDA) with parameter selection. We present a number of simulations using real-world data. The simulations were carried out using 10-fold cross-validation and the results show that the methods achieve comparable performance, yet RBF-DDA had the advantage of building smaller classifiers.


Support Vector Machine Machine Learning Technique Dental Implant Hide Unit Training Pattern 
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  1. 1.
    David, V., Sanchez, A.: Advanced support vector machines and kernel methods. Neurocomputing 55, 5–20 (2003)CrossRefGoogle Scholar
  2. 2.
    Webb, A.: Statistical Pattern Recognition, 2nd edn. Wiley, Chichester (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Barry, M., Kennedy, D., Keating, K., Schauperl, Z.: Design of dynamic test equipment for the testing of dental implants. Materials & Design 26(3), 209–216 (2005)Google Scholar
  4. 4.
    Berthold, M., Diamond, J.: Constructive training of probabilistic neural networks. Neurocomputing 19, 167–183 (1998)CrossRefGoogle Scholar
  5. 5.
    Berthold, M.R., Diamond, J.: Boosting the performance of RBF networks with dynamic decay adjustment. In: Tesauro, G., et al. (eds.) Advances in Neural Information Processing, vol. 7, pp. 521–528. MIT Press, Cambridge (1995)Google Scholar
  6. 6.
    Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, Software (2001), available at
  7. 7.
    Cortes, C., Vapnik, V.: Support-vector network. Machine Learning, 273–297 (1995)Google Scholar
  8. 8.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  9. 9.
    Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artificial Intelligence in Medicine 34(2), 113–127 (2005)CrossRefGoogle Scholar
  10. 10.
    Osuna, F.G.E.E., Freund, R.: Support vector machines: Training and application. Technical Report A.I. Memo 1602, MIT A.I. Lab (1997)Google Scholar
  11. 11.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification (2004), Available at
  12. 12.
    Hui, D., Hodges, J., Sandler, N.: Predicting cumulative risk in endosseous dental implant failure. Journal of Oral and Maxillofacial Surgery 62, 40–41 (2004)CrossRefGoogle Scholar
  13. 13.
    Laine, P., Salo, A., Kontio, R., Ylijoki, S., Lindqvist, C.: Failed dental implants - clinical, radiological and bacteriological findings in 17 patients. Journal of Cranio-Maxillofacial Surgery 33, 212–217 (2005)CrossRefGoogle Scholar
  14. 14.
    Meyer, D., Leisch, F., Hornik, K.: The support vector machine under test. Neurocomputing 55, 169–186 (2003)CrossRefGoogle Scholar
  15. 15.
    Oliveira, A.L.I., Melo, B.J.M., Meira, S.R.L.: Improving constructive training of RBF networks through selective pruning and model selection. Neurocomputing 64, 537–541 (2005)CrossRefGoogle Scholar
  16. 16.
    Oliveira, A.L.I., Melo, B.J.M., Meira, S.R.L.: Integrated method for constructive training of radial basis functions networks. Electronics Letters 41(7), 429–430 (2005)CrossRefGoogle Scholar
  17. 17.
    Oliveira, A.L.I., Neto, F.B.L., Meira, S.R.L.: Improving RBF-DDA performance on optical character recognition through parameter selection. In: Proc. of the 17th International Conference on Pattern Recognition (ICPR 2004), vol. 4, pp. 625–628. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  18. 18.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)Google Scholar
  19. 19.
    Zell, A.: SNNS - Stuttgart Neural Network Simulator, User Manual, Version 4.2. University of Stuttgart and University of Tubingen (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Adriano L. I. Oliveira
    • 1
  • Carolina Baldisserotto
    • 1
  • Julio Baldisserotto
    • 2
  1. 1.Department of Computing Systems, Polytechnic School of EngineeringPernambuco State UniversityMadalena, RecifeBrazil
  2. 2.Faculdade de OdontologiaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil

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