Periodontitis Risk Assessment using two artificial Neural Networks-A Pilot Study

Rajesh Shankarapillai, Lalit Kumar Mathur, Manju Ananthakrishan Nair, Neema Rai, Aditi Mathur

Abstract


Artificial neural networks are currently used for a variety of complex problem solving approaches where a conventional method may not be feasible. Aims & objectives: Levenberg Marquardt and Scaled Conjugate Gradient feed forward back propagation neural network algorithms were compared to assess accuracy for periodontitis risk prediction. Materials and Method: In the present study 230 subjects were assessed for major and minor periodontitis risk factors such as; age, gender, family history of periodontitis, history of periodontal surgery, diet, smoking history, pan chewing habit, history of diabetes, history of hypertension, presence of sub gingival restorations, bleeding on probing, debris index (OHI-S), average pocket probing depth, presence of root calculus, presence of furcation involvement and vertical bone loss. Periodontitis risk assessment was done on a grade of 1 to 5. Results: The Levenberg Marquardt algorithm performed considerably better than Scaled Conjugate Gradient algorithm by converging faster with lesser iterations and produced minimum mean square error in both training and simulation phases. Conclusion: A properly trained neural network with Levenberg Marquardt back propagation algorithm can effectively be used for periodontitis risk prediction.


Keywords


Artificial Neural Networks;Periodontitis Risk Assessment;Neural Network Algorithms;Levenberg Marquardt Algorithm

References


Brickley MR, Shepherd JP, Armstrong RA. Neural networks: a new technique for development of decision support systems in dentistry. J Dent 1998; 26: 305-309.

Ainamo J, Ainamo A. Risk assessment of recurrence of disease during supportive periodontal care epidemiological considerations. J Clin Periodontol 1996; 23: 232-239.

Page RC, Krall EA, Martin J, Mancl L, Garcia RI. Validity and accuracy of a risk calculator in predicting periodontal disease. J Am Dent Assoc 2002; 133: 569-576.

Demuth H, Beale M. Neural network toolbox. MathWorks Version 2001; 4.

Møller M. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993; 6: 525-533.

Löe H, Silness J. Periodontal disease in pregnancy I. Prevalence and severity. Acta Odontologica 1963; 21: 533-551.

Greene J, Vermillion J. The simplified oral hygiene index. Journal of the American Dental Association (1939) 1964; 68: 7.

Ahmed FE. Artificial neural networks for diagnosis and survival prediction in colon cancer. Mol Cancer 2005; 4: 29.

Hagan M, Menhaj M. Training feedforward networks with the Marquardt algorithm. IEEE transactions on Neural Networks 1994; 5: 989-993.

Trivedy CR, Craig G, Warnakulasuriya S. The oral health consequences of chewing areca nut. Addict Biol 2002; 7: 115-125.

Chang Y, Lii C, Tai K, Chou M. Adverse effects of arecoline and nicotine on human periodontal ligament fibroblasts in vitro. Journal of Clinical Periodontology 2001; 28: 277-282.

Page RC, Martin J, Krall EA, Mancl L, Garcia R. Longitudinal validation of a risk calculator for periodontal disease. J Clin Periodontol 2003; 30: 819-827.

Page RC, Martin JA, Loeb CF. The Oral Health Information Suite (OHIS): its use in the management of periodontal disease. J Dent Educ 2005; 69: 509-520.


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