Predicting Osteoporosis Prevalence Based on Ambient-Air Pollution Using Artificial Intelligence

• 발행기관: 한국산업기술융합학회(구. 산업기술교육훈련학회)
• 저자명: Septika Prismasari Kyuseok Kim Hye Young Mun Jung Yun Kang
• 간행물 정보: 『산업기술연구논문지』제29권 3호, 15~22쪽, 전체 8쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2024.09.30

국문 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.

목차

Ⅰ. Introduction

Ⅱ. Methodology

Ⅲ. Results

Ⅳ. Discussion

Ⅴ. Conclusion

References

키워드

Air Pollution Artificial Intelligence Bone Particulate Matter Osteoporosis