DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
Source abstract
Abstract
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. Classified as one of the most prevalent oral health issues, research on dental caries has been carried out for early detection due to pain and cost of treatment. Medical research in oral healthcare has shown limitations such as considerable funds and time required; therefore, artificial intelligence has been used in recent years to develop models that can predict the risk of dental caries. The data used in our study were collected from a children’s oral health survey conducted in 2018 by the Korean Center for Disease Control and Prevention. Several Machine Learning algorithms were applied to this data, and their performances were evaluated using accuracy, F1-score, precision, and recall. Random forest has achieved the highest performance compared to other machine learnings methods, with an accuracy of 92%, F1-score of 90%, precision of 94%, and recall of 87%. The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Oral microbiology and periodontitis research · Dental Health and Care Utilization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer and Electronics Convergence Engineering, Sun Moon University, Asan-si 31460, Korea
Genome-Based BioIT Convergence Institute, Sun Moon University, Asan 31460, Korea
Funding
National Research Foundation of Korea
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