Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study
ML models are an effective approach to develop prediction models for survival analysis. Furthermore, MetS and its components do not seem to influence PCa susceptibility, in contrast to first-degree family history of PCa, age, alcohol consumption, and income.
Authors
- Jeonghee Lee (ORCID: https://orcid.org/0000-0001-9072-6507)
- Junetae Kim (ORCID: https://orcid.org/0000-0002-4278-3491)
- Jeongseon Kim (ORCID: https://orcid.org/0000-0002-0889-2686)
- Hyunsoon Cho (ORCID: https://orcid.org/0000-0002-3261-3114)
- Sun‐Young Kim (ORCID: https://orcid.org/0000-0002-7110-3395)
- Tao Thi Tran (ORCID: https://orcid.org/0000-0002-5273-7093)
Institutions
- Hue University (VN)
- National Cancer Center (KR)
Publication Details
- Journal
- BMC Public Health
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1186/s12889-024-20852-8
- Citations
- 1
- Primary Topic
- Cancer, Lipids, and Metabolism
- Type
- article
- Field-Weighted Citation Impact
- 0.29
Funders
- National Research Foundation
- National Research Foundation of Korea