Machine learning prediction and interpretability analysis of high-risk chest pain: a study from the MIMIC-IV database
Our optimized LightGBM model provides clinically significant predictive capability for high-risk chest pain, offering emergency physicians a decision-support tool to enhance diagnostic accuracy and patient outcomes.
Authors
- H. S. Chen (ORCID: https://orcid.org/0009-0008-0777-3942)
- Junyu Zhang (ORCID: https://orcid.org/0000-0001-6658-3278)
- Zheng Gong (ORCID: https://orcid.org/0000-0002-6538-7344)
- Qingxiong Yang (ORCID: https://orcid.org/0000-0002-4378-2335)
- Wenjun Ding (ORCID: https://orcid.org/0000-0001-7718-9612)
- Xiaojun Fang
- Chen Huang (ORCID: https://orcid.org/0000-0002-4978-493X)
- Jun Ke (ORCID: https://orcid.org/0000-0001-6027-7659)
- Hongyu Huang (ORCID: https://orcid.org/0000-0002-0866-6312)
- Haiyang Song
Institutions
- Fujian Medical University (CN)
- Xiamen University (CN)
- Guangxi Normal University (CN)
- Fuzhou General Hospital of Nanjing Military Command (CN)
- Fuzhou Pulmonary Hospital of Fujian (CN)
- Fujian Provincial Hospital (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-06-30
- DOI
- https://doi.org/10.3389/fphys.2025.1594277
- Citations
- 3
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 4.16