Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS)
Gender, age, ethnicity, and region are significant factors influencing CVD incidence. Although the LGB model demonstrates good overall performance, its low F1 score and recall rate reveal limitations in identifying high-risk cardiovascular disease patients.
Authors
- Bo Shi (ORCID: https://orcid.org/0000-0003-0352-9973)
- Fuyong Hu (ORCID: https://orcid.org/0000-0001-6940-6832)
- Zi-Hao Jiang
- Juan Meng (ORCID: https://orcid.org/0000-0003-1941-6068)
- Shu Li (ORCID: https://orcid.org/0000-0001-7603-5865)
- Qing Huang (ORCID: https://orcid.org/0000-0001-7177-6605)
- Jing Mi
Institutions
- Bengbu Medical College (CN)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1186/s12889-025-21609-7
- Citations
- 18
- Primary Topic
- Healthcare Systems and Public Health
- Type
- article
- Field-Weighted Citation Impact
- 24.38
Funders
- Anhui Provincial Department of Education