@article{MAKHILLJEAS20116113210,
    title = {Neural Networks Based Prediction of Periodontal Disease Using Non-Intrusively Obtained Data},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {6},
    number = {1},
    pages = {6-9},
    year = {2011},
    issn = {1816-949x},
    doi = {jeasci.2011.6.9},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2011.6.9},
    author = {J.,D.,M.A.,S. and},
    keywords = {predicting,Neural networks,periodontal disease,non-intrusive,data,algorithms},
    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.}
    }