Employing Fuzzy Cognitive Map for Periodontal Disease Assessment

Periodontal disease is a chronic bacterial infection that affects the gums and bone supporting the teeth. This research work aims to assess the severity level of periodontal disease in dental patients. The presence or absence of sign-symptoms and risk factors make it a complicated diagnostic task. Dentist usually relies on his knowledge, expertise and experiences to design the treatment(s). Therefore, it is found that there is a variation among treatments administered by different dentists. The methodology of Fuzzy Cognitive Maps (FCM) was used to model this problem and then to calculate the severity of the periodontal disease. The relationships between different sign-symptoms have been defined using easily understandable linguistic terms following the construction process of FCM and then converted to numeric values using Mamdani inference method. For convenience, a graphical interface of the system has been designed based on FCM modeling and reasoning.

[1]  P. P. Groumpos,et al.  Fuzzy cognitive maps: a soft computing technique for intelligent control , 2000, Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No.00CH37147).

[2]  Vijay Kumar Mago,et al.  Fuzzy logic based expert system for the treatment of mobile tooth. , 2011, Advances in experimental medicine and biology.

[3]  Elpiniki I. Papageorgiou.  Medical Decision Making through Fuzzy Computational Intelligent Approaches , 2009, ISMIS.

[4]  Vijay Kumar Mago,et al.  Clinical decision support system for dental treatment , 2012, J. Comput. Sci..

[5]  R. Page,et al.  Histopathologic features of the initial and early stages of experimental gingivitis in man. , 1975, Journal of periodontal research.

[6]  Peter B. Borwein,et al.  uzzy cognitive maps and cellular automata : An evolutionary approach or social systems modelling , 2012 .

[7]  Voula C. Georgopoulos,et al.  Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions , 2007, Soft Comput..

[8]  Schroeder He,et al.  Pathogenesis of inflammatory periodontal disease. A summary of current work. , 1976 .

[9]  Elpiniki I. Papageorgiou,et al.  Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder , 2011, Expert Syst. Appl..

[10]  Jose L. Salmeron,et al.  A Fuzzy Grey Cognitive Maps-based Decision Support System for radiotherapy treatment planning , 2012, Knowl. Based Syst..

[11]  Athanasios K. Tsadiras,et al.  Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps , 2008, Inf. Sci..

[12]  Jose L. Salmeron,et al.  Forecasting Risk Impact on ERP Maintenance with Augmented Fuzzy Cognitive Maps , 2012, IEEE Transactions on Software Engineering.

[13]  Jose L. Salmeron,et al.  Benchmarking main activation functions in fuzzy cognitive maps , 2009, Expert Syst. Appl..

[14]  Rod Taber,et al.  Knowledge processing with Fuzzy Cognitive Maps , 1991 .

[15]  Thomas Torsney-Weir,et al.  A fuzzy cognitive map of the psychosocial determinants of obesity , 2012, Appl. Soft Comput..

[16]  R Attström,et al.  Clinical and histologic characteristics of normal gingiva in dogs. , 1975, Journal of periodontal research.

[17]  M. Listgarten,et al.  Experimental gingivitis in the monkey. Relationship of leukocyte counts in junctional epithelium, sulcus depth, and connective tissue inflammation scores. , 1973, Journal of periodontal research.

[18]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[19]  Jose L. Salmeron,et al.  Ranking fuzzy cognitive map based scenarios with TOPSIS , 2012, Expert Syst. Appl..

[20]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[21]  Chrysostomos D. Stylios,et al.  An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps , 2003, IEEE Transactions on Biomedical Engineering.

[22]  Masoud Nikravesh,et al.  Soft computing for information processing and analysis , 2005 .

[23]  Vijay Kumar Mago,et al.  A Decision Making System for the Treatment of Dental Caries , 2008, Soft Computing Applications in Business.

[24]  Elpiniki I. Papageorgiou,et al.  Fuzzy Cognitive Map Based Approach for Assessing Pulmonary Infections , 2009, ISMIS.

[25]  Bart Kosko,et al.  Virtual Worlds as Fuzzy Cognitive Maps , 1993, Presence: Teleoperators & Virtual Environments.

[26]  H. E. Schroeder,et al.  Lymphocyte-fibroblast interaction in the pathogenesis of inflammatory gingival disease , 2005, Experientia.

[27]  Voula C. Georgopoulos,et al.  Augmented Fuzzy Cognitive Maps Supplemented with Case Based Reasoning for Advanced Medical Decision Support , 2005 .

[28]  Panagiota Spyridonos,et al.  Brain tumor characterization using the soft computing technique of fuzzy cognitive maps , 2008, Appl. Soft Comput..

[29]  Chrysostomos D. Stylios,et al.  Fuzzy Cognitive Maps in modeling supervisory control systems , 2000, J. Intell. Fuzzy Syst..