# DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
###### 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. machine learning; dental caries; random forest; logistic regression; gradient boot decision tree; support vector machine; artificial neural network; convolutional neural network; long short time memory 2022123043390/app120630433300/app120630433300/  
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number of permanent teeth experienced with caries (decayed, missing, or filled). Oral health is known to be affected by an individual's improper oral health management habits, eating habits, use of dental care, and economic factors [8]. Hence, identifying DMFT and its related factors and predicting permanent teeth with dental caries can be an essential basis for establishing an individual oral prevention strategy. In Korea, in accordance with Article 9 of the Oral Health Act, children's oral health surveys are being conducted. This survey is used as basic data for effective dental caries management for children's oral health by examining the public's oral health status, related forms, and use of dental care products. The survey conducted in 2018 surveyed 27,568 people, and it is recognized as big data in the field of oral epidemiology. The survey results show that DMFT tends to increase significantly as age increases [10]. Machine learning (ML) has become an essential instrument to comprehend and analyze data as massive as the survey mentioned above and is being used in various ways in the medical area. ML is a technique used to predict future outcomes by learning existing patterns between elements of targeted data [11]. The history of ML started in the 1950s and progressed until the 1980s and 1990s and then stood still. However, in the mid-2000s, the vast amount of big data accumulated on the Internet and hardware development to handle it amounted to a colossal leap forward [12]. Recently, remarkable achievements have been made in image recognition, speech recognition, and translation using ML. In the field of dentistry in Korea, deep learning (DL) research using panoramic, dental computed tomography, X-ray, and intraoral photos are being conducted [13; 14]. However, compared to the importance and usefulness of the technology, it is not being used smoothly in dentistry, and attempts to use it in more diverse fields are needed. Unlike the conventional method, ML can predict curious teeth in the population with surveys or basic information before performing a detailed diagnosis by an expert. Human resources, time, and cost required for an oral health examination will considerably be reduced. ML can also help classify the high-risk individuals to receive an accurate diagnosis and necessary treatment from a specialist. In addition, if the predictive model identifies contributing factors that significantly impact DMFT, caries prevention can be accomplished by controlling those factors.