Supervised machine learning-based classification of oral malodor based on the microbiota in saliva samples.
OBJECTIVE
This study presents an effective method of classifying oral malodor from oral microbiota in saliva by using a support vector machine (SVM), an artificial neural network (ANN), and a decision tree. This approach uses concentrations of methyl mercaptan in mouth air as an indicator of oral malodor, and peak areas of terminal restriction fragment (T-RF) length polymorphisms (T-RFLPs) of the 16S rRNA gene as data for supervised machine-learning methods, without identifying specific species producing oral malodorous compounds.


METHODS
16S rRNA genes were amplified from saliva samples from 309 subjects, and T-RFLP analysis was carried out with the DNA fragments. T-RFLP analysis provides information on microbiota consisting of fragment lengths and peak areas corresponding to bacterial strains. The peak area is equivalent to the frequency of a specific fragment when one molecule is selected from terminal fragments. Another frequency is obtained by dividing the number of species-containing samples by the total number of samples. An SVM, an ANN, and a decision tree were trained based on these two frequencies in 308 samples and classified the presence or absence of methyl mercaptan in mouth air from the remaining subject.


RESULTS
The proportion that trained SVM expressed as entropy achieved the highest classification accuracy, with a sensitivity of 51.1% and specificity of 95.0%. The ANN and decision tree provided lower classification accuracies, and only classification by the ANN was improved by weighting with entropy from the frequency of appearance in samples, which increased the accuracy to 81.9% with a sensitivity of 60.2% and a specificity of 90.5%. The decision tree showed low classification accuracy under all conditions.


CONCLUSIONS
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.
Department of Chemistry, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chuo-ku, Tokyo 101-8310, Japan. Electronic address: nakano.yoshio70@nihon-u.ac.jp.
Yoshio
Y
Nakano
Section of Preventive and Public Health Dentistry, Division of Oral Health, Growth and Development, Kyushu University Faculty of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi 812-8582, Japan.
Toru
T
Takeshita
Section of Preventive and Public Health Dentistry, Division of Oral Health, Growth and Development, Kyushu University Faculty of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi 812-8582, Japan.
Noriaki
N
Kamio
Section of Preventive and Public Health Dentistry, Division of Oral Health, Growth and Development, Kyushu University Faculty of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi 812-8582, Japan.
Susumu
S
Shiota
Section of Preventive and Public Health Dentistry, Division of Oral Health, Growth and Development, Kyushu University Faculty of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi 812-8582, Japan.
Yukie
Y
Shibata
Section of General Dentistry, Department of General Dentistry, Fukuoka Dental College, 2-15-1 Tamura, Sawara-ku, Fukuoka-shi 814-0193, Japan.
Nao
N
Suzuki
Center for Oral Diseases, Fukuoka Dental College, 3-2-1 Hakataekimae, Hakata-ku, Fukuoka-shi 812-0011, Japan.
Masahiro
M
Yoneda
Section of General Dentistry, Department of General Dentistry, Fukuoka Dental College, 2-15-1 Tamura, Sawara-ku, Fukuoka-shi 814-0193, Japan.
Takao
T
Hirofuji
Section of Preventive and Public Health Dentistry, Division of Oral Health, Growth and Development, Kyushu University Faculty of Dental Science, 3-1-1 Maidashi, Higashi-ku, Fukuoka-shi 812-8582, Japan.
Yoshihisa
Y
Yamashita
D001185:Artificial Intelligence; D001483:Base Sequence; D017931:DNA Primers; D003663:Decision Trees; D006209:Halitosis / Q000145:classification* / Q000382:microbiology; D006801:Humans; D064307:Microbiota; D012150:Polymorphism, Restriction Fragment Length; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D012463:Saliva / Q000382:microbiology*; D060388:Support Vector Machine
D017931:DNA Primers; D012336:RNA, Ribosomal, 16S
Neural networks; Oral malodor classification; Support vector machines