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Journal of Computational Science
Volume 3, Issue 5, September 2012, Pages 254-261
Clinical decision support system for dental treatment
Author links open overlay panelVijay KumarMagoaAnjaliMagod
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https://doi.org/10.1016/j.jocs.2012.01.008
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Abstract
Background
In this research, a decision making system, based on fuzzy inference mechanism as proposed by Mamdani, is presented. Literature suggests that there is a lack of consistency among dentists in choosing treatment plan(s). So, this research work aims to facilitate the dentist decide the treatment plan(s) of the broken tooth.

Methods
An expert system based on fuzzy logic has been designed to accept inaccurate and vague values of dental signs and symptoms associated with the broken tooth. We designed a knowledge base with 60 rules and used Mamdani inference algorithm to decide the possible one or more treatment(s) and suggest the same to the dentist.

Results
The results proposed by the system are compared with the dentists’ suggestions. The Chi-square test of homogeneity is conducted on 100 randomly generated sample cases with the help of three professional dentists. It is found that the results produced by the system are consistent with the treatment plan(s) proposed by the dentists. Chi-square value of the test is 3.843565 which is less than the critical value which is 12.592. Hence, we are unable to reject the null hypothesis that assumes the two populations are homogeneous with respect to treatments.

Conclusions
The accuracy of the proposed decision support system for the treatment of broken tooth enhances the confidence level of the dentists while making decision regarding the treatment plan(s). Simple and interactive GUI makes it easy to use.

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Keywords
Fuzzy logicClinical decision makingDental treatmentChi-square test
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Vijay Kumar Mago received his Masters degree in Computer Applications in 2001 from Guru Nanak Dev University, India and PhD in Computer Science in 2010 from Panjab University, India. He is currently working as a postdoctoral fellow at The MoCSSy Program, The IRMACS Centre, Simon Fraser University, Canada. His research interests include decision making in multi-agent environment, probabilistic networks, neural networks and fuzzy logic based expert systems. He has served on the program committees of many international conferences and workshops. He is also associated with various international journals as reviewer and is editing books with IGI Global, USA and Springer, Germany. He has 14 research papers to his credit.

Nitin Bhatia received his Graduate degree in Science in 1998 and Master’s degree in Computer Applications in 2001, from Guru Nanak Dev University, Amritsar, India. He is working as Assistant Professor in the Department of Computer Science, DAV College, India. He is also pursuing his Ph.D. from Punjabi University, Patiala, India. He has 15 research papers to his credit. His areas of interest are pattern recognition, computer vision and fuzzy logic. He is associated with various international journals as reviewer and is editing a book entitled, “Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies” for IGI Global, USA to be released in December 2011.

Ajay Bhatia received the B.Sc. in Information Technology and the Masters in Computer Application from Guru Nanak Dev University and ICFAI University in 2004 and 2007 respectively. He is serving as Assistant Professor in the department of computer science at Rayat Bahra Institute of Management, Punjab, India. His research interests include probabilistic intelligence, multi-agent systems, Web 2.0 and mathematical computing languages.

Anjali Mago received her Bachelor’s degree in Dental Surgery in 2001 from Baba Farid University, India and M.Phil. in Hospital and Health System Management from BITS, Pilani, India in 2011. She is currently pursuing Ph.D. from School of Population and Public Health, University of British Columbia, Canada. Her research interests include decision making systems in dentistry and conducting need assessment of dentists for online education.

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