Predicting Osteoporosis Prevalence Based on Ambient-Air Pollution Using Artificial Intelligence
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Ambient-air pollution is a recognized risk factor for numerous health problems, including osteoporosis. Previous studies demonstrated that air pollution, particularly that caused by particulate matter, can be a modifiable risk factor for osteoporosis. Therefore, this study proposes an artificial-intelligence-based model to predict the prevalence of osteoporosis caused by air-pollution exposure in South Korea. We conducted a retrospective cohort study using publicly available data from 2015 to 2023. Air-quality data were obtained from the Korean Statistical Information Service, and the number of osteoporosis was retrieved from the Health Insurance Review and Assessment Service database. A deep neural network (DNN) model was developed to predict the prevalence of osteoporosis. The model demonstrated promising prediction accuracies ranging from 85.31% to 86.78%, with a mean absolute percentage error of 13.95%. These findings suggest the potential application of DNN models for predicting the prevalence of osteoporosis based on ambient-air pollution. The present study serves as a valuable foundation for the further investigation and development of predictive models for osteoporosis.
Abstract
Ambient-air pollution is a recognized risk factor for numerous health problems, including osteoporosis. Previous studies demonstrated that air pollution, particularly that caused by particulate matter, can be a modifiable risk factor for osteoporosis. Therefore, this study proposes an artificial-intelligence-based model to predict the prevalence of osteoporosis caused by air-pollution exposure in South Korea. We conducted a retrospective cohort study using publicly available data from 2015 to 2023. Air-quality data were obtained from the Korean Statistical Information Service, and the number of osteoporosis was retrieved from the Health Insurance Review and Assessment Service database. A deep neural network (DNN) model was developed to predict the prevalence of osteoporosis. The model demonstrated promising prediction accuracies ranging from 85.31% to 86.78%, with a mean absolute percentage error of 13.95%. These findings suggest the potential application of DNN models for predicting the prevalence of osteoporosis based on ambient-air pollution. The present study serves as a valuable foundation for the further investigation and development of predictive models for osteoporosis.
참고문헌 (19)
- Y. H. Lin, "Air Pollutants Interaction and Gender Difference on Bone Mineral Density T-score in Taiwanese Adults", International Journal of Environmental Research and Public Health, 17(24), pp.1~15 (2022)[DOI]
- X. H. Yu, "Air Pollution, Genetic Factors and The Risk of Osteoporosis : A Prospective Study in the UK Biobank", Frontiers in Public Health, 11, pp.1~11 (2023)[DOI]
- J. Shin, "Incidence of Osteoporosis and Ambient Air Pollution in South Korea : A Population-Based Retrospective Cohort Study", BMC Public Health, 21(1794), pp.1~9 (2021)
- G. Adami, "Association Between Exposure to Fine Particulate Matter and Osteoporosis : A Population-Based Cohort Study", Osteoporosis International, 33, pp.169~176 (2022)[DOI]
- K. L. Pang, "Particulate Air Pollution and Osteoporosis : A Systematic Review", Risk Management and Healthcare Policy, 14, pp.2715~2732 (2021)[DOI]
- D. Prada, "Association of Air Particulate Pollution with Bone Loss Over Time and Bone Fracture Risk : Analysis of Data from Two Independent Studies", The Lancet Planetary Health, 1(8), pp.e337~e347 (2017)
- K. Y. Lee, "Air Pollution Exposure and Osteoporosis Among Retired Workers with Chronic Obstructive Pulmonary Disease", Occupational Medicine & Health Affairs, 2(3), pp.167~171 (2014)[DOI]
- Y. Ji, "Regression Analysis of Air Pollution and Pediatric Respiratory Diseases Based on Interpretable Machine Learning", Frontiers in Earth Science, 11, pp.1~13 (2023)[DOI]
- M. Hung, "Application of Machine Learning for Diagnostic Prediction of Root Caries", Gerodontology, 36(4), pp.395~404 (2019)[DOI]
- S. Du, "Deep Air Quality Forecasting Using Hybrid Deep Learning Framework", IEEE Transactions on Knowledge and Data Engineering, 33(6), pp.2414~2424 (2019)
- S. Subramaniam, "Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health : A Narrative Review", Sustainability, 14(16), pp.1~36 (2022)[DOI]
- H. Alzoubi, "Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations", Sensors, 23(9), pp.4439~ (2023)
- L. Liu, "Explainable Deep Transfer Learning Model for Disease Risk Prediction Using High-Dimensional Genomic Data", PLOS Computational Biology, 18(7), pp.1010328~ (2022)
- S. Prismasari, "A Proposal for a Predictive Model for the Number of Patients with Periodontitis Exposed to Particulate Matter and Atmospheric Factors Using Deep Learning", Journal of Dental Hygiene Science, 24(1), pp.22~28 (2024)[DOI]
- "Our World in Data, Coronavirus Pandemic Country Profile : South Korea" (2022)[URL]
- Lewis, C. D., "Industrial and Business Forecasting Methods : A Practical Guide to Exponential Smoothing and Curve Fitting", pp.40~ (1982), Butterworth Scientific
- J. J. Liu, "Association Between Outdoor Particulate Air Pollution and The Risk of Osteoporosis : A Systematic Review and Meta-Analysis", Osteoporosis International, 32, pp.1911~1919 (2021)[DOI]
- F. Pouresmaeili, "A Comprehensive Overview on Osteoporosis and Its Risk Factors", Therapeutics and Clinical Risk Management, 14, pp.2029~2049 (2018)[DOI]
- L. R. Chen, "Nutritional Support and Physical Modalities for People with Osteoporosis : Current Opinion", Nutrients, 11(12), pp.1~15 (2019)[DOI]