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THIRD MOLAR MATURITY INDEX IN INDONESIAN JUVENILES: COMPARING LINEAR AND POLYNOMIAL KERNEL PERFORMANCE IN SUPPORT VECTOR REGRESSION FOR DENTAL AGE ESTIMATION

*Rizky Merdietio Boedi orcid scopus  -  Department of Dentistry, Universitas Diponegoro, Indonesia
Rosalina Intan Saputri orcid  -  Faculty of Dentistry, Maranatha Christian University, Indonesia
Open Access Copyright (c) 2022 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Dental age estimation is a branch of forensic odontology that plays a pivotal role in identifying, examining, or determining the legal status of the living and the dead. This research explores the capability of support vector regression to estimate chronological age from the third molar maturity index (I3M) in Indonesian Juveniles and compares the linear and kernel performance. Two hundred and twenty-two orthopantomo-graphy were measured using I3M in the lower left third molar and processed using R Studio with Caret extension. The analysis was separated into two groups, group 1 using only I3M as a predictor, and group 2 using both I3M and sex. Both groups were analyzed using SVR with the linear and polynomial kernel. The result suggests that using polynomial kernel SVR in group 1 produces the best results, with an R2 value of 0.64, RMSE of 1.588 years, and MAE of 1.25 years using degree = 3, c = 0.25. However, the addition of a sex predictor in the model reduces its accuracy when using the polynomial kernel.

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Keywords: Forensic Dentistry; Dental Age Estimation;Third Molar; Third Molar Maturity Index; Support Vector Regression,;Polynomial Kernel

Article Metrics:

  1. Altan, A., & Akbulut, N. (2019). Does the Angulation of an Impacted Mandibular third Molar Affect the Prevalence of Preoperative Pathoses? Journal of Dentistry, 20(1), 48
  2. Angelakopoulos, N., De Luca, S., Velandia Palacio, L. A., Coccia, E., Ferrante, L., & Cameriere, R. (2018). Third Molar Maturity Index (I3M) for Assessing Age of Majority: Study of a Black South African Sample. International Journal of Legal Medicine, 132(5), 1457-1464. https://doi.org/10.1007/s00414-018-1818-4
  3. Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012). The'K'in K-fold Cross Validation. Proc. of the 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium
  4. Balla, S. B., Banda, T. R., Galic, I., N, N. M., & Naishadham, P. P. (2019). Validation of Cameriere's Third Molar Maturity Index Alone and in Combination with Apical Maturity of Permanent Mandibular Second Molar for Indicating Legal Age of 14 Years in a Sample of South Indian Children. Forensic Science International, 297, 243-248. https://doi.org/10.1016/j.forsciint.2019.02.009
  5. Balla, S. B., Lingam, S., Kotra, A., P, H. R., P, K., N, N. M., & Cameriere, R. (2019). New Regression Models for Dental Age Estimation in Children Using Third Molar Maturity Index: A Preliminary Analysis Testing its Usefulness as Reliable Age Marker. Legal Medicine (Tokyo, Japan), 39, 35-40. https://doi.org/10.1016/j.legalmed.2019.06.003
  6. Ben-Hur, A., Ong, C. S., Sonnenburg, S., Scholkopf, B., & Ratsch, G. (2008). Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology, 4(10), e1000173. https://doi.org/10.1371/journal.pcbi.1000173
  7. Bjork, M. B., & Kvaal, S. I. (2018). CT and MR imaging used in age estimation: a systematic review. JFOS -Journal of Forensic Odonto-Stomatology, 36(1). http://www.iofos.eu/Journals/JFOS May18/JFOS-36-1-14.pdf
  8. Bouckaert, R. R. (2003). Choosing Between Two Learning Algorithms Based on Calibrated Tests. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC
  9. Cameriere, R., Ferrante, L., & Cingolani, M. (2004). Variations in Pulp/Tooth Area Ratio as an Indicator of Age: a Preliminary Study. Journal of Forensic Sciences, 49(2), 1-3. https://doi.org/10.1520/jfs2003259
  10. Cameriere, R., Ferrante, L., & Cingolani, M. (2006). Age Estimation in Children by Measurement of Open Apices in Teeth. International Journal of Legal Medicine, 120(1), 49-52. https://doi.org/10.1007/s00414-005-0047-9
  11. Cameriere, R., Ferrante, L., De Angelis, D., Scarpino, F., & Galli, F. (2008). The Comparison Between Measurement of Open Apices of Third Molars and Demirjian Stages to Test Chronological Age of Over 18 Year Olds in Living Subjects. International Journal of Legal Medicine, 122(6), 493-497. https://doi.org/10.1007/s00414-008-0279-6
  12. Cardoza, A. R. (2004). Dental Forensic Identification in the 2003 Cedar Fire. Journal - California Dental Association, 32(8), 689-693. https://www.ncbi.nlm.nih.gov/pubmed/15481236
  13. Carter, K., & Worthington, S. (2015). Morphologic and Demographic Predictors of Third Molar Agenesis: a Systematic Review and Meta-analysis. Journal of Dental Research, 94(7), 886-894. https://doi.org/10.1177/0022034515581644
  14. Chaillet, N., Willems, G., & Demirjian, A. (2004). Dental Maturity in Belgian Children Using Demirjian's Method and Polynomial Functions: New Standard Curves for Forensic and Clinical Use. Journal of Forensic Odonto-Stomatology, 22(2), 18-27
  15. De Luca, S., Pacifici, A., Pacifici, L., Polimeni, A., Fischetto, S. G., Velandia Palacio, L. A., Vanin, S., & Cameriere, R. (2016). Third Molar Development by Measurements of Open Apices in an Italian Sample of Living Subjects. Journal of Forensic and Legal Medicine, 38, 36-42. https://doi.org/10.1016/j.jflm.2015.11.007
  16. De Tobel, J., Hillewig, E., Bogaert, S., Deblaere, K., & Verstraete, K. (2017). Magnetic Resonance Imaging of Third Molars: Developing a Protocol Suitable for Forensic Age Estimation. Annals of Human Biology, 44(2), 130-139. https://doi.org/10.1080/03014460.2016.1202321
  17. Duangto, P., Iamaroon, A., Prasitwattanaseree, S., Mahakkanukrauh, P., & Janhom, A. (2017). New Models for Age Estimation and Assessment of Their Accuracy Using Developing Mandibular Third Molar Teeth in a Thai Population. International Journal of Legal Medicine, 131(2), 559-568. https://doi.org/10.1007/s00414-016-1467-4
  18. Gomez Jimenez, L., Velandia Palacio, L. A., De Luca, S., Ramirez Vasquez, Y., Corominas Capellan, M., & Cameriere, R. (2019). Validation of the Third Molar Maturity Index (I3M): Study of a Dominican Republic Sample. Journal of Forensic Odonto-Stomatology, 3(37), 27-33. https://www.ncbi.nlm.nih.gov/pubmed/31894135
  19. Gunn, S. R. (1998). Support Vector Machines for Classification and Regression. ISIS technical report, 14(1), 5-16
  20. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A Practical Guide to Support Vector Classification
  21. Kuhn, M. (2008). Building Predictive Models in R Using the Caret Package. J Stat Softw, 28(5), 1-26. https://doi.org/DOI 10.18637/jss.v028.i05
  22. Kvaal, S., & Solheim, T. (1994). A Non-Destructive Dental Method for Age Estimation. Journal of Forensic Odonto-Stomatology, 12(1), 6-11. https://www.ncbi.nlm.nih.gov/pubmed/9227083
  23. Macha, M., Lamba, B., Avula, J. S. S., Muthineni, S., Margana, P. G. J. S., & Chitoori, P. (2017). Estimation of Correlation Between Chronological Age, Skeletal Age and Dental Age in Children: A Cross-Sectional Study. Journal of clinical and diagnostic research: JCDR, 11(9), ZC01. https://doi.org/10.7860/Jcdr/2017/25175.10537
  24. Nelson, S. J., & Ash, M. M. (2010). Wheeler's Dental Anatomy, Physiology, and Occlusion (9th ed. ed.). Saunders/Elsevier
  25. Prabowo, Y. B., Ermanto, H., Skripsa, T. H., Limijadi, E. K., & Merdietio Boedi, R. (2020). Aplikasi Metode Third Molar Maturity Index pada Kelompok Usia Remaja. e-GiGi, 8(2)
  26. Pretty, I. A., & Sweet, D. (2001). A look at forensic dentistry – Part 1: The Role of Teeth in The Determination of Human Identity. British Dental Journal, 190(7), 359-366. https://doi.org/10.1038/sj.bdj.4800972
  27. Rai, B., Kaur, J., & Jafarzadeh, H. (2010). Dental Age Estimation from the Developmental Stage of the Third Molars in Iranian Population. Journal of Forensic and Legal Medicine, 17(6), 309-311. https://doi.org/https://doi.org/10.1016/j.jflm.2010.04.010
  28. Rozylo-Kalinowska, I., Kalinowski, P., Kozek, M., Galic, I., & Cameriere, R. (2018). Validity of the Third Molar Maturity Index I3M for Indicating the Adult Age in the Polish Population. Forensic Science International, 290, 352 e351-352 e356. https://doi.org/10.1016/j.forsciint.2018.06.034
  29. Saputri, R. I. (2020). Dental Age Estimation of Indonesian Population: A Literature Review. SONDE (Sound of Dentistry), 5(1), 13-21
  30. Smola, A. J. (1996). Regression Estimation with Support Vector Learning Machines. Master’s Thesis, Technische Universität München
  31. Tang, F., Tino, P., Gutierrez, P. A., & Chen, H. (2015). The Benefits of Modeling Slack Variables in SVMs. Neural Computation, 27(4), 954-981. https://doi.org/10.1162/NECO_a_00714
  32. Team, R. (2020). RStudio: Integrated Development for R. In http://www.rstudio.com/
  33. Vapnik, V. (2013). The Nature of Statistical Learning Theory. Springer science & business media
  34. Wong, T.-T., & Yeh, P.-Y. (2020). Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Transactions on Knowledge and Data Engineering, 32(8), 1586-1594. https://doi.org/10.1109/tkde.2019.2912815
  35. Zhang, F., & O'Donnell, L. J. (2020). Support Vector Regression. In A. Mechelli & S. Vieira (Eds.), Machine Learning (pp. 123-140). Academic Press. https://doi.org/10.1016/b978-0-12-815739-8.00007-9

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