Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients
Based on T2DM data and machine learning theory, a Bayesian-optimized XgBoost model was established using the RFE+LightGBM method. This model effectively determines whether T2DM patients have CHD.
Authors
- Minjie Duan
- Chunyu Wang (ORCID: https://orcid.org/0000-0003-0813-2801)
- Tianyu Xiang
- Xiaozhu Liu (ORCID: https://orcid.org/0000-0002-7800-3100)
- Haodong Huang (ORCID: https://orcid.org/0000-0002-6109-8860)
- Yingxi Chen
Institutions
- Capital Medical University (CN)
- Sichuan University (CN)
- West China Second University Hospital of Sichuan University (CN)
- Beijing Shijitan Hospital (CN)
- Chongqing Science and Technology Commission (CN)
- Children's Hospital of Chongqing Medical University (CN)
- Chongqing Municipal Health Commission (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-05-22
- DOI
- https://doi.org/10.3389/fendo.2025.1550793
- Citations
- 4
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 11.75
Funders
- Chongqing Medical University