Abstract
The aim of this research work is to design an expert system to assist dentist in treating the mobile tooth. There is lack of consistency among dentists in choosing the treatment plan. Moreover, there is no expert system currently available to verify and support such decision making in dentistry. A Fuzzy Logic based expert system has been designed to accept imprecise and vague values of dental sign-symptoms related to mobile tooth and the system suggests treatment plan(s). The comparison of predictions made by the system with those of the dentist is conducted. Chi-square Test of homogeneity is conducted and it is found that the system is capable of predicting accurate results. With this system, dentist feels more confident while planning the treatment of mobile tooth as he can verify his decision with the expert system. The authors also argue that Fuzzy Logic provides an appropriate mechanism to handle imprecise values of dental domain.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Khanna V, Karjodkar FR (2009) Decision Support Systems in Dental Decision Making: An Introduction. Journal of Evidence-Based Dental Practice 9(2):73–76
Mago VK, Prasad B, Bhatia A, Mago A (2008) A Decision Making System for the Treatment of Dental Caries. In: Bhanu Prasad (ed) Soft Computing Applications in Business. Studies in Fuzziness and Soft Computing, Vol 230. Springer, Germany
Thomas J, Zwemer (1993) Boucher’s Clinical Dental Terminology: A Glossary of Accepted Terms in All Disciplines of Dentistry, 4th ed. Mosby, St. Louis, USA
Wikipedia.org (2009) http://en.wikipedia.org/wiki/Fuzzy{ _}logic. Accessed October 2009
Ross TJ, Booker JM, Parkinson WJ (2002) Fuzzy Logic and Probability Applications: Bridging the Gap. Society for Industrial and Applied Mathematics, Philadelphia, and American Statistical Association, Alexandria, Virginia
Zadeh LA (1965) Fuzzy Sets. Information and Control 3:338–353
Fathi-Torbaghan M, Meyer D (1994) MEDUSA: A Fuzzy Expert System for Medical Diagnosis of Acute Abdominal Pain. Methods of Information in Medicine 33(5):522–529
Saritas I, Allahverdi N, Sert I U (2003) A Fuzzy Expert System Design for Diagnosis of Prostate Cancer. In: Rachev B, Smrikarov A (ed) Proceedings of the 4th International Conference Conference on Computer Systems and Technologies: E-Learning (Rousse, Bulgaria, June 19–20, 2003) ACM, New York
Allahverdi N, Torun S, Saritas I (2007) Design of a Fuzzy Expert System for Determination of Coronary Heart Disease Risk. In: Rachev B, Smrikarov A, Dimov D (ed) Proceedings of the 2007 International Conference on Computer Systems and Technologies (Bulgaria, June 14–15, 2007) ACM, New York
Watsuji T, Arita S, Shinohara S et al (1999) Medical application of fuzzy theory to the diagnostic system of tongue inspection in traditional Chinese medicine. In: IEEE International Fuzzy Systems Conference Proceedings (Seoul, South Korea, August 22–25, 1999) doi: 10.1109/FUZZY.1999.793222
Kuo H-C, Chang H-K, Wang Y-Z (2004) Symbiotic Evolution-Based Design of Fuzzy-Neural Diagnostic System for Common Acute Abdominal Pain. Expert Systems with Applications 27(3):391–401
Wu M, Zhou C, Lin K (2007) An Intelligent TCM Diagnostic System Based on Intuitionistic Fuzzy Set. In: Proceedings of the Fourth international Conference on Fuzzy Systems and Knowledge Discovery. Doi: http://doi.ieeecomputersociety.org/10.1109/FSKD.2007.169
Schuh Ch, Hiesmayr M, Kaipel M et al (2004) Towards an intuitive expert system for weaning from artificial ventilation. In: Proceedings of IEEE Annual Meeting of the Fuzzy Information. doi: 10.1109/NAFIPS.2004.1337445
Zadeh LA (1988) Fuzzy Logic. Computer. doi:10.1109/2.53
Mathworks.com (2009) http://www.mathworks.com Accessed October 2009
Merer R, Nieuwland J, Zbinden, AM et al (1992) Fuzzy Logic Control of Blood Pressure During Anesthesia. IEEE Control Systems Magazine. 12(9):12–17
Bouchon-Meunier B (1995) Fuzzy Logic and Soft Computing. World Scientific Publishing Co, New Jersey
Dualibe C, Verleysen M, Jespers PGA (2003) Design of Analog Fuzzy Logic Controllers in Cmos Technologies. Kluwer Academic Publisher, The Netherlands
Brennian TA (1992) An Empirical Analysis of Accidents and Accident Law: The Case of Medical Malpractices Law. St. Louis University Law Journal 36:823–878
Yen J, Langari R (1999) Fuzzy Logic: Intelligence, Control, and Information. Prentice-Hall, New Jersey
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Mago, V.K., Mago, A., Sharma, P., Mago, J. (2011). Fuzzy Logic Based Expert System for the Treatment of Mobile Tooth. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_62
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7046-6_62
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7045-9
Online ISBN: 978-1-4419-7046-6
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)