J Dent Hyg Sci.
2024 Mar;24(1):22-28. 10.17135/jdhs.2024.24.1.22.
A Proposal for a Predictive Model for the Number of Patients with Periodontitis Exposed to Particulate Matter and Atmospheric Factors Using Deep Learning
- Affiliations
-
- 1Department of Dental Hygiene, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju 26493, Korea
- 2Department of Environmental Planning, Seoul National University, Seoul 08826, Korea
- KMID: 2554534
- DOI: http://doi.org/10.17135/jdhs.2024.24.1.22
Abstract
- Background
Particulate matter (PM) has been extensively observed due to its negative association with human health. Previous research revealed the possible negative effect of air pollutant exposure on oral health. However, the predictive model between air pollutant exposure and the prevalence of periodontitis has not been observed yet. Therefore, this study aims to propose a predictive model for the number of patients with periodontitis exposed to PM and atmospheric factors in South Korea using deep learning.
Methods
This study is a retrospective cohort study utilizing secondary data from the Korean Statistical Information Service and the Health Insurance Review and Assessment database for air pollution and the number of patients with periodontitis, respectively. Data from 2015 to 2022 were collected and consolidated every month, organized by region. Following data matching and management, the deep neural networks (DNN) model was applied, and the mean absolute percentage error (MAPE) value was calculated to ensure the accuracy of the model.
Results
As we evaluated the DNN model with MAPE, the multivariate model of air pollution including exposure to PM2.5, PM10, and other atmospheric factors predict approximately 85% of the number of patients with periodontitis. The MAPE value ranged from 12.85 to 17.10 (mean±standard deviation=14.12±1.30), indicating a commendable level of accuracy.
Conclusion
In this study, the predictive model for the number of patients with periodontitis is developed based on air pollution, including exposure to PM2.5, PM10, and other atmospheric factors. Additionally, various relevant factors are incorporated into the developed predictive model to elucidate specific causal relationships. It is anticipated that future research will lead to the development of a more accurate model for predicting the number of patients with periodontitis.
