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An Analytical Study for Gum Disease Detection Systems


Author(s):

Kirti Nagane , JSPM’S Bhivarabai Sawant Intitute of Technology and Research(BSIOTR), Savitribai Phule Pune University ,India; Divya Jadhav, JSPM’S Bhivarabai Sawant Intitute of Technology and Research(BSIOTR),Savitribai Phule Pune University,India; Nikita Dongre, JSPM’S Bhivarabai Sawant Intitute of Technology and Research(BSIOTR),Savitribai Phule Pune University ,India; Anshita Dhar, JSPM’S Bhivarabai Sawant Intitute of Technology and Research(BSIOTR), Savitribai Phule Pune University ,India, JSPM’S Bhivarabai Sawant Intitute of Technology and Research(BSIOTR), Savitribai Phule Pune University ,India

Keywords:

Periodontal disease, Gum disease, Gingivitis, Dempster-Shafer reasoning, Neural Network

Abstract:

Now-a-days people have hardly spare time to consult the dentist as per their convenience. Emerging gum disease is silent destroyer of tissues of gums affecting mainly adults aged above 30 years. The system designed to detect two diseases early stage-gingivitis, which later on leads to severe chronic bacterial infection called periodontitis. Artificial Intelligent techniques are used for diagnosing the diseases caused to gum using dempster-shafer reasoning. Many methodologies are in existence to analyze the symptoms and risk factors affecting the gums like Artificial Neural Networks(ANN). Most of the system related to gum disease detection are suffering from performance issues like time and space complexities. The advance methodologies used in the system for predicting the symptoms precisely using Hidden Markov Model(HMM). The incentive of paper is to predict possibilities of patients based on the estimated symptoms and evaluating results and analyzing whether patient is suffered from gum disease problem or not. This paper eventually studies many more aspects of gum diseases detection to analyze in detail so that some contribution can be made in future.


Other Details:

Manuscript Id :IJSTEV3I5026
Published in :Volume : 3, Issue : 5
Publication Date: 01/12/2016
Page(s): 94-97
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