Development of machine learning models for predicting postoperative hyperglycemia in non-diabetic gastric cancer patients: a retrospective cohort study analysis
A novel machine learning-based model, utilizing multi-dimensional perioperative features, can accurately predict the risk of POH in non-diabetic patients with gastric cancer. The SVM-radial model demonstrated superior predictive performance and clinical interpretability, providing a viable tool for early risk stratification and personalized glycemic management in the surgical setting.
Authors
- C. Fei
- Peibei Duan (ORCID: https://orcid.org/0000-0003-2946-5517)
- Nan Wang (ORCID: https://orcid.org/0000-0001-7686-7012)
- Li Yang (ORCID: https://orcid.org/0000-0002-7880-2113)
- Ding Ye (ORCID: https://orcid.org/0000-0001-6654-7832)
- Jie Zhang (ORCID: https://orcid.org/0000-0003-2140-3231)
Institutions
- Nanjing University of Chinese Medicine (CN)
- Jiangsu Province Hospital (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-11-10
- DOI
- https://doi.org/10.3389/fendo.2025.1687745
- Citations
- 1
- Primary Topic
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Type
- article
- Field-Weighted Citation Impact
- 1.29
Funders
- Government of Jiangsu Province