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August Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the August 2021 Edition of the journal. The last date of research paper submission is July 15, 2021.

Dental Expert System

Oladele Tinuke O, Sanni Yetunde Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2015
© 2013 by IJAIS Journal
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  1. Oladele Tinuke O and Sanni Yetunde. Article: Dental Expert System. International Journal of Applied Information Systems 8(2):1-15, January 2015. BibTeX

    	author = "Oladele Tinuke O and Sanni Yetunde",
    	title = "Article: Dental Expert System",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 2,
    	pages = "1-15",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"


The early 20th century brought along a better understanding of dental disease and prevention. However, advancement in computer technology has encouraged researchers to develop software for assisting doctors in making decision without consulting the specialists directly. Software development exploits the potential of human intelligence such as reasoning, making decision, learning by experience and many others. The software was not meant to replace the specialist or doctor, yet it was developed to assist general practitioners and specialist in diagnosing and predicting patient's condition from certain rules or experience. The goal of this paper is to demonstrate the practical applicability of Information and Communication Technology (ICT) for the diagnosis of dental ailments based on a set of symptoms. Expert system is a computer system that emulates the decision-making ability of a human expert. Expert system in medical applications reduces cost, time, human expertise and medical error. This paper on Dental Expert System is a desktop based application designed to replace the manual system used by most Medical organizations in treatment. This paper is aimed at emphasizing on the use of expert systems to diagnose mild dental problems. In this paper, the ED Expert System which is referred to as Electronic Dentist was developed based on the Coactive Neuro-Fuzzy Expert System Model and implemented by using C# programming language. The expert system is a simple and user friendly desktop application which could be used by anyone so as to complement the manual process of diagnosis.


  1. Atkinson, J. et al. (2002), Electronic patient records for dental school clinics: more than paperless systems. - Journal of Dental Education, 66, Vol. 5, 634 - 642.
  2. Ledley, R. S. and Lusted, L. B. (1959). "Reasoning Foundations of Medical Diagnosis". Science 130: 9–21. Doi: 10. 1126/science. 130. 3366. 9. JSTOR 1758070.
  3. Hoong, N. K. (1988). Medical Information Science - Framework and Potential. In Proceedings of the International Seminar and Exhibition Computerization for Development-the Research Challenge, (pages 191-198). Kuala Lumpur: Universiti Pertanian Malaysia.
  4. Shortliffe, E. H. (1987). Programs to Support Clinical Decision Making. JAMA. Vol. 258, Issue 1 pages 61-67. American Medical Association.
  5. Szolovits, P. ,Doyle, J. ,Long, W. J. , Kohane, I. and Pauker. S. G. (1994). Guardian Angel: Patient-Centered Health Information Systems. TR-604, Massachusetts Institute of Technology, Laboratory for Computer Science, 545 Technology Square, Cambridge, MA, 02139.
  6. Shortliffe, E. H. (1998). The Evaluation of Health-Care Records in the Era of the Internet. http://smi-web. stanford. edu/pubs/abstracts_by_author/
  7. Rusovick, R. , Warner, D, (1997). The Webification of Medicine: Interventional Informatics through the WWW. http://www. pulsar. org/febweb/papers/mwww3. htm Sarle. Journal of the American Medical Association.
  8. Chellappa, M. (1995). Telemedic-Care. NCIT'95: 8'th National Conference Information Technology'95 (16-18). Gabungan Komputer Nasional Malaysia.
  9. Warner, D. (1997). Malaysian Medical Matrix: Telemedicine in the age of the Multimedia.
  10. Djam, X. Y. , Wajiga, G. M. , Kimbi, Y. H. and Blamah, N. V. (2011). A Fuzzy Expert System for the Management of Malaria. International Journal of Pure and Applied Sciences and Technology. 5(2), 84-108.
  11. Donfack, A. F. , Abdullahi, M. , Ezugwu, A. E. and Alkali, S. A. (2009). Online system for Diagnosis and Treatment of Malaria.
  12. Adekoya, A. F. , Akinwale, A. T. and Oke, O. E. (2008). A Medical Expert System for Managing Tropical Diseases. Proceedings of the Third Conference on Science and National Development. 74-86.
  13. Obot, O. U. and Uzoka, F. M. E. (2008). Fuzzy Rule-Based Framework for the Management of Tropical Diseases. International Journal of Engineering and Informatics, 1(1), 7-17.
  14. Djam, X. Y. and Kimbi, Y. H. (2011). Fuzzy Expert System for the Management of Hypertension. The Pacific Journal of Science and Technology. 12(1), 390-402.
  15. Djam, X. Y. and Kimbi, Y. H. (2011). A Decision Support System for Tuberculosis Diagnosis. The Pacific Journal of Science and Technology. 12(2), 410-425. http://www. akamaiuniversity. us/PJST. htm.
  16. Agboizebeta, I. A. and Chukwuyeni, O. J. (2012). Application of Neuro-fuzzy Expert System for the Probe and Prognosis of Thyroid Disorder. International Journal of Fuzzy logic Systems (IJFLS), 2 (2), 1-11.
  17. Kandel, A. (1991). Fuzzy Expert Systems CRC Press ISBN: 084934297x.
  18. Sugeno, M. and Kang, E. T. (1988). Structure Identification of Fuzzy model. Fuzzy Sets and Systems. 28, 15-33.
  19. Takagi T. and Sugeno M. (1985). Fuzzy Identification of Systems and its Application in Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics, 15, 116-132.
  20. Kosko, B. (1991). Neural networks and fuzzy systems: a dynamical systems approach. Prentice Hall. Upper Saddle River. NJ.
  21. Mamdani, E. H. & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies. 7(1), 1-13.
  22. Lee, C. -C (1990) (a). Fuzzy logic in Control Systems: Fuzzy Logic Controller – Part I. IEEE Transactions on Systems, Man and Cybernetics, 20(2), 404-418.
  23. Lee, C. -C (1990) (b). Fuzzy logic in Control Systems: fuzzy Logic Controller- Part 2. IEEE Transactions on Systems, Man and Cybernetics. 20(2), 419-435.
  24. Jang, J-S. R (1991)c. Self-learning fuzzy controller based on temporal back-propagation. IEEE Transaction on neural network, 3, 714-723.
  25. Jang, J-S. R. (1993)a. ANFIS: Adaptive network based fuzzy inference systems. IEEE Transaction on Systems, Man, and Cybernetics, 23, 665-685.
  26. Jang, J-S. R, (1993)b. Predicting chaotic time series with fuzzy if then rules. In Proceedings of IEEE International Conference on Fuzzy System. San Francisco.
  27. Wang, M. Q. and Hirschberg, J. (1992). Automatic classification of intonational phrasing boundaries. Computer Speech and Language, 6(2):175-196.
  28. Horikawa, S. . , Furuhashi, T. and Uchikawa, Y. (1992), On fuzzy modelling using fuzzy neural networks with the back-propagation algorithm, IEEE Trans. Neural Networks, 3(5), pp. 801-806,
  29. Hinton, G. E. (1989). Connectionist Learning Procedures. Artificial Intelligence, 40(1), 143-150.
  30. Kosko, B. (1990), Stability and Adaptation of Neural Networks. Published by PN.
  31. Rumelhart, D. E. , Hinton, G. E. and Willians, R. J. (1986). Learning internal representations by error propagation. In D. E, Rumelhart and James L. McClelland, editors, parallel distributed processing: explorations in the microstructure of cognition, volumes 1, chapter 8, pages 318-362. MIT Press, Cambridge, MA.
  32. Moody, J. and Darken C. J. (1989). Fast Learning in Networks of Locally-tuned Processing units. Neural Computation, 1, 281-294.
  33. Huang, W. Y. and Lippmann R. P. (1988). Neural Net and Traditional Classifiers in Neural Information Processing Systems. New York: American Institute of Physics, 387-396.
  34. Oladele T. O. , Sadiku J. S. and Oladele R. O. (2014), Coactive Neuro-Fuzzy Expert System: A Framework for Diagnosis of Malaria. African Journal of Computing & ICTs (AJOCICT). A Publication of the Computer Chapter of the Institute of Electrical & Electronics Engineers (IEEE) Nigeria Section. . Vol. 7 No. 2. pp 173 – 186. ISSN: 2006-1781.
  35. Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8, 338 – 35.
  36. Hopgood, A. A. (1993). Knowledge-Based Systems for Engineers and Scientists. CRC Press. ISBN 0-8493-8616-0.
  37. Jang, J-S. R. (1991), Fuzzy modeling using generalized neural networks and Kalman filter algorithm. In Proceedings National Conference on Artificial Intelligence (AAAI-91), pp. 762-767.
  38. Jang, J-S. R. (1991)b. A self-learning fuzzy controller with application to automobile tracking problem. In Proceedings IEEE Roundtable Discussion on fuzzy and neural systems, and vehicle application. Tokyo, Japan, Institute of Industrial Science, University of Tokyo. Paper no. 10.
  39. Jang, J-S. R. , and Gulley N. (1995). MATLAB Fuzzy Logic Toolbox User's Guide. Version 1. The Mathworks, Incorporated.
  40. Jang, J-S. R. , Sun, C. T. and Mizutani, E. (1997). Neuro-fuzzy and Soft Computing (A Computational Approach to Learning and Machine Intelligence), Prentice Hall, Inc. pp1-90.
  41. Kandel, A. (1992). Fuzzy Expert Systems. CRC Press, Inc. Boca Raton, Fl.
  42. Kim, C-H. , and Lee, J. (2003). Adaptive Network-based fuzzy Inference Systems with Prunning. SICE Annual Conference in Fukui: Fukui University, Japan.
  43. Klir, G. J. and Yuan B. (1995). Fuzzy Sets and Fuzzy logic. Theory and Practice. Prentice Hall. ISBN 0-13-101171-5.
  44. Kulikowski, C. A. and Weiss, S. M. (1982). Representation of Expert Knowledge for Consultation: The CASNET and EXPERT Projects. In Szolovits, P. (Ed. ) Artificial Intelligence in Medicine. Westview Press, Boulder, Colorado.
  45. Kumar, Y. and Jain Y. (2012). Research aspects of expert system. International Journal of Computing & Business Research. ISSN (online); 2229-6166. Proceedings of' I-society 2012 at GKU, Talwandi Sabo Bathinda, Punjab.
  46. Lin, C-T. , and Lee C. S. G. (1991). Neural-Network-based Fuzzy logic control and Decision system. IEEE Transactions on Computers. 40(12).
  47. Mehdi, N. and Mehdi Y. (2009). Designing a Fuzzy Expert System of Diagnosing the Hepatitis-B Intensity Rate and Comparing it with Adaptive Neural Network fuzzy system. Proceedings of the World Congress on Engineering and Computer Science, Vol. II WCECS 2009, San Francisco, USA.
  48. Miller III, W. T. , Sutton, R. S. and Werbos, P. J. eds. (1990). Neural Networks for Control. MIT Press.
  49. Mizutani, E. and Jang, J-S. R, (1995). Coactive neural fuzzy modeling. IEEE International conference on Neural Network (ICNN'95).
  50. Mizutani, E. Jang, J-S, R. and Nishio, K. (1995). Coactive Neuro-fuzzy modeling for color recipe prediction.
  51. Pomerleau, D. A. (1991). Efficient training of artificial neural networks for autonomous navigation. Neural Computation, 3, 88-97.
  52. Pomerleau, D. A. (1992). Neural network perception for mobile robot guidance. PhD. Thesis, Department of Computer Science, Carnegie Mellon University.
  53. Poole, D. , Mackworth, A. and Goebel, R. (1996). Computational Intelligence: A Logical Approach, Oxford University Press. ISBN 0–19-510270-3.
  54. Psaltis, D. , Sideris, A. and Yamamura, A. (1988). A mutilayered neural Network controller. IEEE Control Systems Magazine. 8(4), 17-21.
  55. Sivanandam, S. N. and Deepa, S. N. (2007). Principles of Soft Computing, Wiley India (P) Ltd.
  56. Sivanandam, S. N. , Sumathi, S. and Deepa, S. N. (2007). Introduction to Fuzzy logic using MATLAB. Springer.
  57. Sugeno, M. , editor (1985). Industrial applications of fuzzy control. Elsevier Science.
  58. Tek, F. B. , Dempster, A. G. and Kale, I. (2009). Computer Vision for Microscopy Diagnosis of Malaria. Malaria Journal. 8,153.
  59. Tsoukalas, L. H. and Uhrig, R. E. (1993). Fuzzy and Neural approaches to Engineering. John Wiley & Son, Inc.
  60. Werbos, P. J. (1991). An overview of neural network for control. IEEE Control Systems Magazine, 11(1), 40-41.
  61. Yasunobu, S. and Miyamoto, S. (1985). Automatic train operation by predictive fuzzy control. In M. Sugeno, editor, Industrial applications of fuzzy control, pages 1-18. North-Holland, Armsterdam.
  62. Zadeh, L. A. (1973). Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. IEEE Transaction Systems, Man and Cybernetics. 3(1), 28-44.
  63. Zilouchian, A and Jamshidi, M,. (2001). Intelligent Control Systems using Soft Computing Methodologies. CRC Press. ISBN 0-8493-1875-0.
  64. http://www. aptronix. com/fuzzynet/app/note/reactor. htm.
  65. http://www. controleng. com/, Temperature Control: PID Vs fuzzy logic.
  66. http://www. fuzy-logic. com fuzzy logic tutorial.
  67. Super Coridor
  68. http://www. pulsar. org/febweb/papers/m3web. htm.
  69. http://www. audubondentalcare. com/index. php?option=com_content&view=article&id=82&Itemid=215282.
  70. http://dentistry. about. com/od/toothmouthconditions/Dental_Problems_Conditions_and_Diseases. htm
  71. http://www. groups. csail. mit. edu/medg/ftp/psz/AIM82/ch1. html.
  72. http://healthinformatics. wikispaces. com/Artificial+Intelligence+in+Medicine.
  73. http://library. thinkquest. org/05aug/00109/medicine. htm
  74. http://www. medicinenet. com/gum_problems/article. htm
  75. http://www. namibiadent. com/History/HistoryDentistry. html.
  76. http://www. openclinical. org/aiinmedicine. html
  77. http://en. wikipedia. org/wiki/Dental_software
  78. http://en. wikipedia. org
  79. Tooth Erosion. jpg, http://www. lookfordiagnosis. com
  80. Padlock. png, http://www. becuo. com


Expert System, Dentist, Patient, Dental Diseases, Fuzzy Logic