Files and links (1)
CC BY V4.0, Open Access
Journal article
Application of Elastic networks and Bayesian networks to explore influencing factors associated with arthritis in middle-aged and older adults in the Chinese community
Frontiers in public health, Vol.13, 1437213
2025
PMID: 40270731
Abstract
Bayesian networks (BNs) are an excellent machine learning algorithm for extensively exploring the influencing factors associated with many diseases. However, few researchers have used BNs to examine the influencing factors associated with arthritis in older adults in the Chinese community. Our aim has been to use BNs to construct a complex network of relationships between arthritis and its related influencing factors and to predict arthritis through Bayesian inference, thereby providing scientific references for its control and prevention. Data were downloaded from the 2015 China Health and Retirement Longitudinal Study (CHARLS) online database, a longitudinal survey of the middle-aged and older adults in China. Twenty-two variables such as smoking, depressive symptoms, age, and joint pain were included in this study. First, Elastic networks (ENs) were used to screen for features closely associated with arthritis, and we subsequently incorporated these features into the construction of the BNs model. We performed structural learning of the BNs based on the taboo algorithm and used the maximum likelihood method for parameter learning of the BNs. In total, 15,764 participants were enrolled in this study, which included 5,076 patients with arthritis. ENs identified 13 factors strongly associated with arthritis. The BNs consisted of 14 nodes and 24 directed edges. Among them, depressive symptoms and age were direct influences on arthritis, whereas gender was an indirect influence on the diseases. BNs graphically visualized the complex network of relationships between arthritis and its influences and predicted the development of arthritis through Bayesian inference. These results were in line with clinical practice. BNs thus have a wide range of application prospects.
Details
- Title
- Application of Elastic networks and Bayesian networks to explore influencing factors associated with arthritis in middle-aged and older adults in the Chinese community
- Authors/Creators
- Tao Zhong - School of Public Health, Hebei Medical University, Shijiazhuang, ChinaTianlun Li - School of Public Health, Hebei Medical University, Shijiazhuang, ChinaJiapei Hu - Graduate School, The Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, ChinaJiayi Hu - Graduate School, The Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, ChinaLi Jin - Graduate School, The First Affiliated Hospital of Soochow University, Suzhou, ChinaYuxuan Xie - School of Public Health, Hebei Medical University, Shijiazhuang, ChinaBin Ma - Murdoch University, School of Medical, Molecular and Forensic SciencesDailun Hu - Department of Pathogenic Biology, Hebei Medical University, Shijiazhuang, China
- Publication Details
- Frontiers in public health, Vol.13, 1437213
- Publisher
- Frontiers Media S.A.
- Number of pages
- 11
- Identifiers
- 991005765127907891
- Copyright
- © 2025 Zhong et al.
- Murdoch Affiliation
- School of Medical, Molecular and Forensic Sciences
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Metrics
3 File views/ downloads
17 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 9 Mathematics
- 9.92 Statistical Methods
- 9.92.2788 Regression Techniques
- Web Of Science research areas
- Public, Environmental & Occupational Health
- ESI research areas
- Social Sciences, general