Predicting isolated impaired glucose tolerance without oral glucose tolerance test using machine learning in Chinese Han men
Machine learning models based on demographic and clinical characteristics offer a cost-effective method for predicting I-IGT in Chinese Han men aged over 50, without the need for an OGTT. These models could complement existing early diagnostic strategies, thereby enhancing the early detection and prevention of diabetes. Additionally, FPG alone could serve as an efficient screening tool for the early identification of I-IGT in clinical settings.
Authors
- Shuangtong Yan (ORCID: https://orcid.org/0000-0003-2344-4504)
- Chunlin Li (ORCID: https://orcid.org/0000-0003-4161-9013)
- Jing Xie (ORCID: https://orcid.org/0000-0003-4942-5125)
- Lin Wang (ORCID: https://orcid.org/0000-0003-2433-8594)
- Yue Ruan (ORCID: https://orcid.org/0000-0002-1299-3746)
- Yanping Gong (ORCID: https://orcid.org/0000-0003-4241-8600)
- Xinyu Miao
- Lichao Ma
- Zhaoyan Gu
- Banruo Sun
Institutions
- Chinese Academy of Sciences (CN)
- Chinese PLA General Hospital (CN)
- Shenzhen Institutes of Advanced Technology (CN)
- Chinese People's Liberation Army (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3389/fendo.2025.1514397
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94