Journal of Engineering and Applied Sciences

Year: 2011
Volume: 6
Issue: 1
Page No. 6 - 9

Neural Networks Based Prediction of Periodontal Disease Using Non-Intrusively Obtained Data

Authors : M.A. Faruqi, S. Shah, R. Agarwala, D. Sun and J. Sai

Abstract: Periodontal disease is a serious worldwide epidemic. It affects not only the dentition of the infected individual but also their overall health. Risk calculators for periodontal disease based on easily obtained data have been in use for years. However due to a number of factors that contribute to the disease, there has been no success in developing a model that provides a notable level of accuracy for predicting the disease patterns. In this study, we have developed neural network algorithms for predicting the presence and severity of periodontal disease in adults. The algorithm is based on dentists’ evaluation and non-intrusively obtained data from patients’ periodontal history. Results obtained from this basic study show that the approach can be used in predicting periodontal disease.

How to cite this article:

M.A. Faruqi, S. Shah, R. Agarwala, D. Sun and J. Sai, 2011. Neural Networks Based Prediction of Periodontal Disease Using Non-Intrusively Obtained Data. Journal of Engineering and Applied Sciences, 6: 6-9.

Design and power by Medwell Web Development Team. © Medwell Publishing 2022 All Rights Reserved