Digital Decision Making In Dentistry: Analysis And Prediction of Periodontitis Using Machine Learning Approach




Machine Learning and Deep Learning, the powerful weapons of Artificial Intelligence plays crucial role and major contributions in almost all the sectors using sophisticated technological framework with an insight to unlock the needs of business enacting decisions where data patterns holds a main role right from data gathering and explorations to visualization and predictions. Recently it is also a predominating technology used vigorously in various health sectors like medical, dental and allied health services as an aid to develop tools for decision making in data analytics and exploration, disease prediction and control, data analytics and treatment planning. Major research on this area was done in past contributing good frameworks for the predictions of Breast cancer, Rheumatoid Arthritis, Osteoporosis, Diabetes, Sarcoidosis, Graves’ disease, AIDS, Psoriasis and many more. The current research paper is a result of using such Machine Learning approaches for the prediction of Periodontitis, a most common gum disease which leads to severe complications like tooth supporting structure loss like bone loss around tooth, ligament loss and finally the tooth loss if left untreated. In the current paper, a dataset of 206 sizes of diabetic and non-diabetic periodontitis patients were collected with the measurable parameters like age, sex, oral hygienic status, tooth mobility, periodontal index, gingival index, furcation, alveolar bone loss, pocket depth and other parameters were taken and implemented. Supervised machine learning algorithms for classification like Support vector machine, Naïve Bayes, Random forest, Logistic regression, decision tree and K Nearest neighbor algorithms were used and implemented in python using jupyter notebook for the prediction of periodontitis and obtained accuracies of 96.7%, 95.1%, 96.7%, 93.5%, 96.7% and 98.3%  respectively. The current paper demonstrates how the dataset was collected and implemented using Machine learning approach in dentistry for obtaining a suggestible predictable model for periodontitis also compares all the models mentioned for their efficiency and accuracies.


How to Cite
J, D., & TK, L. (2022). Digital Decision Making In Dentistry: Analysis And Prediction of Periodontitis Using Machine Learning Approach. International Journal of Next-Generation Computing, 13(3).


  1. Aberin, S. T. A. and Goma, J. C. d. 2018. Detecting periodontal disease using convolutional neural networks. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnol- ogy, Information Technology,Communication and Control, Environment and Management (HNICEM). IEEE, 1–6. DOI:
  2. Al Haidan, A., Abu-Hammad, O., and Dar-Odeh, N. 2014. Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks. Computational and Mathe- matical Methods in Medicine 5. DOI:
  3. Arbabi, S., Jahantigh, F. F., and Moghadam, S. A. 2018. Presenting a model for peri- odontal disease diagnosis using two artificial neural network algorithms. Health Scope 7, 3, e65330. DOI:
  4. Arigbede, A. O., Babatope, B. O., and Bamidele, M. K. 2012. Periodontitis and systemic diseases: A literature review. Journal of Indian Society of Periodontology 16, 4, 487. DOI:
  5. Berdouses, E. D., Koutsouri, G. D., Tripoliti, E. E., Matsopoulos, G. K., Oulis,
  6. C. J., and Fotiadis, D. I. 2015. A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images. Computers in biology and medicine 62, 119–135. DOI:
  7. Berrar, D. 2018. Bayes’ theorem and naive bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics 403. DOI:
  8. Biau, G. 2012. Analysis of a random forests model. The Journal of Machine Learning Re- search 13, 1, 1063–1095.
  9. Chen, H., Zhang, K., Lyu, P., Li, H., Zhang, L., Wu, J., and Lee, C.-H. 2019. A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films. Scientific reports 9, 1, 1–11. DOI:
  10. Clarke, N. G. and Hirsch, R. S. 1990. Periodontitis and angular alveolar lesions: a critical distinction. Oral surgery, oral medicine, oral pathology 69, 5, 564–571. DOI:
  11. Dr. Sajili Mittal, D. P. K. e. 2012. Tooth mobility: A review. Heal Talk 5, 40–42.
  12. Farhadian, M., Shokouhi, P., and Torkzaban, P. 2020. A decision support system based on support vector machine for diagnosis of periodontal disease. BMC Research Notes 13, 1, 1–6. DOI:
  13. Feres, M., Louzoun, Y., Haber, S., Faveri, M., Figueiredo, L. C., and Levin, L. 2018. Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles. International dental journal 68, 1, 39–46. DOI:
  14. Geetha, V., Aprameya, K., and Hinduja, D. M. 2020. Dental caries diagnosis in digi- tal radiographs using back-propagation neural network. Health Information Science and Systems 8, 1, 1–14. DOI:
  15. Grossi, S. G. and Genco, R. J. 1998. Periodontal disease and diabetes mellitus: a two-way relationship. Annals of periodontology 3, 1, 51–61. DOI:
  16. Gupta, S. and Gupta, M. K. 2021. Computational prediction of cervical cancer diagnosis using ensemble-based classification algorithm. The Computer Journal . DOI:
  17. Hossin, M. and Sulaiman, M. N. 2015. A review on evaluation metrics for data classification evaluations. International journal of data mining and knowledge management process 5, 2, 1. DOI:
  18. Jiang, L., Chen, D., Cao, Z., Wu, F., Zhu, H., and Zhu, F. 2022. A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health 22, 1, 1–9. DOI:
  19. Kim, J. and Amar, S. 2006. Periodontal disease and systemic conditions: a bidirectional relationship. Odontology 94, 1, 10–21. DOI:
  20. Kinane, D. F., Stathopoulou, P. G., and Papapanou, P. N. 2017. Periodontal diseases. DOI:
  21. Nature reviews Disease primers 3, 1, 1–14.
  22. Kirti Nagane, Nikita Dongre, A. D. and Jadhav, D. 2017. Enriching gum disease predic- tion using machine learning. International Journal of Science Technology Engineering 3, 11, 273–278.
  23. Lakshmi, T. K. and Dheeba, J. 2020. Digitalization in dental problem diagnosis, prediction and analysis: a machine learning perspective of periodontitis. Int. J. Recent Technol. Eng 8, 5, 67–74. DOI:
  24. Lee, J.-H., Kim, D.-H., Jeong, S.-N., and Choi, S.-H. 2018a. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of dentistry 77, 106–111. DOI:
  25. Lee, J.-H., Kim, D.-h., Jeong, S.-N., and Choi, S.-H. 2018b. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of periodontal and implant science 48, 2, 114–123. DOI:
  26. Lin, P., Huang, P., and Huang, P. 2017. Automatic methods for alveolar bone loss degree measurement in periodontitis periapical radiographs. Computer methods and programs in biomedicine 148, 1–11. DOI:
  27. Lo¨e, H. 1967. The gingival index, the plaque index and the retention index systems. The Journal of Periodontology 38, 6, 610–616. DOI:
  28. Maalouf, M. 2011. Logistic regression in data analysis: an overview. International Journal of Data Analysis Techniques and Strategies 3, 3, 281–299. DOI:
  29. Machoy, M. E., Szyszka-Sommerfeld, L., Vegh, A., Gedrange, T., and Wo´zniak, K. 2020. The ways of using machine learning in dentistry. Advances in clinical and experimental medicine: official organ Wroclaw Medical University 29, 3, 375–384. DOI:
  30. Moriyama, Y., Lee, C., Date, S., Kashiwagi, Y., Narukawa, Y., Nozaki, K., and Mu- rakami, S. 2019. A mapreduce-like deep learning model for the depth estimation of peri- odontal pockets. In HEALTHINF. 388–395. DOI:
  31. Noble, W. S. 2006. What is a support vector machine? Nature biotechnology 24, 12, 1565–1567. DOI:
  32. Nordland, W. P. and Tarnow, D. P. 1998. A classification system for loss of papillary height. Journal of periodontology 69, 10, 1124–1126. DOI:
  33. Ozden, F., Ozgonenel, O., Ozden, B., and Aydogdu, A. 2015. Diagnosis of periodontal diseases using different classification algorithms: a preliminary study. Nigerian journal of clinical practice 18, 3, 416–421. DOI:
  34. Papantonopoulos, G., Takahashi, K., Bountis, T., and Loos, B. G. 2014. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic pa- rameters. PloS one 9, 3, e89757. DOI:
  35. Patel, H. H. and Prajapati, P. 2018. Study and analysis of decision tree based classification algorithms. International Journal of Computer Sciences and Engineering 6, 10, 74–78. DOI:
  36. Patil, S., Kulkarni, V., and Bhise, A. 2019. Algorithmic analysis for dental caries detection using an adaptive neural network architecture. Heliyon 5, 5, e01579. DOI:
  37. Pilloni, A. and Rojas, M. A. 2018. Furcation involvement classification: a comprehensive review and a new system proposal. Dentistry Journal 6, 3, 34. DOI:
  38. Pitones-Rubio, V., Cha´vez-Cortez, E., Hurtado-Camarena, A., Gonza´lez-Rasco´n, A., and Seraf´ın-Higuera, N. 2020. Is periodontal disease a risk factor for severe covid- 19 illness? Medical hypotheses 144, 109969. DOI:
  39. Polson, A. M. and Goodson, J. M. 1985. Periodontal diagnosis: current status and future needs. Journal of Periodontology 56, 1, 25–34. DOI:
  40. Poulsen, S. 1981. Epidemiology and indices of gingival and periodontal disease. Pediatr Dent 3, 82–88.
  41. Preshaw, P. M. 2015. Detection and diagnosis of periodontal conditions amenable to prevention. DOI:
  42. BMC oral health 15, 1, 1–11.
  43. Rad, A. E., Rahim, M. S. M., Kolivand, H., and Norouzi, A. 2018. Automatic computer- aided caries detection from dental x-ray images using intelligent level set. Multimedia Tools and Applications 77, 21, 28843–28862. DOI:
  44. Ramya, R., Prabu, D., Naveen, N., and Vidya, P. 2013. Cigarette smoking, snuff use and alcohol drinking: the associated risk behaviour for oral health in young indian males. alcohol 9, 10.
  45. Sa´nchez-Ota´lvaro, L.-M., Jime´nez-Rivero, Y., Velasquez, R.-A., and Botero, J.-E.
  46. Development and testing of a mobile application for periodontal diagnosis. Journal of Clinical and Experimental Dentistry 14, 3, e269.
  47. Shaju, J. P., Zade, R., and Das, M. 2011. Prevalence of periodontitis in the indian population: A literature review. Journal of Indian Society of Periodontology 15, 1, 29. DOI:
  48. Shiau, H. J. 2018. Periodontal disease in women and men. Current Oral Health Reports 5, 4, 250–254. DOI:
  49. Thakur, A., Guleria, P., and Bansal, N. 2016. Symptom and risk factor based diagnosis of gum diseases using neural network. In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence). IEEE, 101–104. DOI:
  50. Tonetti, M. S., Jepsen, S., Jin, L., and Otomo-Corgel, J. 2017. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. Journal of clinical periodontology 44, 5, 456–462. DOI:
  51. Vamos, C. A., Thompson, E. L., Avendano, M., Daley, E. M., Quinonez, R. B., and Boggess, K. 2015. Oral health promotion interventions during pregnancy: a systematic review. Community dentistry and oral epidemiology 43, 5, 385–396. DOI:
  52. Zhang, Z. 2016. Introduction to machine learning: k-nearest neighbors. Annals of translational medicine 4, 11. DOI: