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Lee, Semin
Computational Biology Lab
Research Interests
  • Cancer genomics & single-cell genomics

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Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

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Title
Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number
Author
Kim, Eun-HyeKim, SeunghoonKim, Hyun-JooJeong, Hyoung-ohLee, JaewoongJang, JinhoJoo, Ji-YoungShin, YerangKang, JihoonPark, Ae KyungLee, Ju-YounLee, Semin
Issue Date
2020-11
Publisher
FRONTIERS MEDIA SA
Citation
FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY, v.10, pp.571515
Abstract
Periodontitis is a widespread chronic inflammatory disease caused by interactions between periodontal bacteria and homeostasis in the host. We aimed to investigate the performance and reliability of machine learning models in predicting the severity of chronic periodontitis. Mouthwash samples from 692 subjects (144 healthy controls and 548 generalized chronic periodontitis patients) were collected, the genomic DNA was isolated, and the copy numbers of nine pathogens were measured using multiplex qPCR. The nine pathogens are as follows: Porphyromonas gingivalis (Pg), Tannerella forsythia (Tf), Treponema denticola (Td), Prevotella intermedia (Pi), Fusobacterium nucleatum (Fn), Campylobacter rectus (Cr), Aggregatibacter actinomycetemcomitans (Aa), Peptostreptococcus anaerobius (Pa), and Eikenella corrodens (Ec). By adding the species one by one in order of high accuracy to find the optimal combination of input features, we developed an algorithm that predicts the severity of periodontitis using four machine learning techniques. The accuracy was the highest when the models classified "healthy" and "moderate or severe" periodontitis (H vs. M-S, average accuracy of four models: 0.93, AUC = 0.96, sensitivity of 0.96, specificity of 0.81, and diagnostic odds ratio = 112.75). One or two red complex pathogens were used in three models to distinguish slight chronic periodontitis patients from healthy controls (average accuracy of 0.78, AUC = 0.82, sensitivity of 0.71, and specificity of 0.84, diagnostic odds ratio = 12.85). Although the overall accuracy was slightly reduced, the models showed reliability in predicting the severity of chronic periodontitis from 45 newly obtained samples. Our results suggest that a well-designed combination of salivary bacteria can be used as a biomarker for classifying between a periodontally healthy group and a chronic periodontitis group.
URI
https://scholarworks.unist.ac.kr/handle/201301/49283
URL
https://www.frontiersin.org/articles/10.3389/fcimb.2020.571515/full
DOI
10.3389/fcimb.2020.571515
ISSN
2235-2988
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BME_Journal Papers
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