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Enriching Gum Disease Prediction using Machine Learning


Author(s):

Kirti Nagane , JSPM's Bhivrabai Sawant Institute Of Technology And Research; Nikita Dongre, JSPM's Bhivrabai Sawant Institute Of Technology And Research; Anshita Dhar, JSPM's Bhivrabai Sawant Institute Of Technology And Research; Divya Jadhav, JSPM's Bhivrabai Sawant Institute Of Technology And Research

Keywords:

Gingivitis, Periodontitis, K-Means, Shannon Information Gain, Baum Welch Algorithm, Dempster-Shaffer Reasoning

Abstract:

Gum disease being the awful circumstance faced by people at the moment, promptly requires prediction of the same with least flaws. The system designed in this paper assists in detecting the disease by performing the prognosis at very initial level with all possible symptoms. The most common form of gum diseases are gingivitis and periodontitis can be detected using techniques of Machine Learning. "Hidden Marcov Model" applied along with Dempster Shaffer Reasoning in this system, helps in diagnosis of the symptoms that might be hidden. Most of the techniques may have performance snag related to Gum Disease Detection Systems scarcely not produce desired output. This paper concentrates on overcoming the issues relevant to the performance by proposing a novel idea for Gum Disease Detection Systems. The software developed emphasizes on disease detection and predicting whether the patient is suffering from gum disease by taking input as symptoms along with their images that is processed using clustering methodologies. The desktop application thus generates the resultants in the form of ranges by processing over the provided symptoms and images.


Other Details:

Manuscript Id :IJSTEV3I11125
Published in :Volume : 3, Issue : 11
Publication Date: 01/06/2017
Page(s): 273-278
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