Artif Intell Med - Supervised machine learning-based classification of oral malodor based on the microbiota in saliva samples.


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JECTIVE: 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.

Resumo Limpo

jectiv studi present effect method classifi oral malodor oral microbiota saliva use support vector machin svm artifici neural network ann decis tree approach use concentr methyl mercaptan mouth air indic oral malodor peak area termin restrict fragment trf length polymorph trflps s rrna gene data supervis machinelearn method without identifi specif speci produc oral malodor compoundsmethod s rrna gene amplifi saliva sampl subject trflp analysi carri dna fragment trflp analysi provid inform microbiota consist fragment length peak area correspond bacteri strain peak area equival frequenc specif fragment one molecul select termin fragment anoth frequenc obtain divid number speciescontain sampl total number sampl svm ann decis tree train base two frequenc sampl classifi presenc absenc methyl mercaptan mouth air remain subjectresult proport train svm express entropi achiev highest classif accuraci sensit specif ann decis tree provid lower classif accuraci classif ann improv weight entropi frequenc appear sampl increas accuraci sensit specif decis tree show low classif accuraci conditionsconclus use trf proport frequenc model classifi presenc methyl mercaptan volatil sulfurcontain compound caus oral malodor develop svm classifi success classifi presenc methyl mercaptan high specif classif expect use screen saliva oral malodor visit specialist clinic classif svm ann requir identif oral microbiota speci respons malodor ann also requir proport trfs

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