Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10320554 | Artificial Intelligence in Medicine | 2014 | 5 Pages |
Abstract
Using T-RF proportions and frequencies, models to classify the presence of methyl mercaptan, a volatile sulfur-containing compound that causes oral malodor, were developed. SVM classifiers successfully classified the presence of methyl mercaptan with high specificity, and this classification is expected to be useful for screening saliva for oral malodor before visits to specialist clinics. Classification by a SVM and an ANN does not require the identification of the oral microbiota species responsible for the malodor, and the ANN also does not require the proportions of T-RFs.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yoshio Nakano, Toru Takeshita, Noriaki Kamio, Susumu Shiota, Yukie Shibata, Nao Suzuki, Masahiro Yoneda, Takao Hirofuji, Yoshihisa Yamashita,