Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance
Utilizing SMOTE for class balancing, Boruta for feature selection, GSCV for optimal hyperparameter tuning, and LightGBM for model development achieved strong predictive performance for IHM following AMI. These findings underscore the significance of robust processing and careful algorithm selection.
Authors
- Peng Lei
- Bing Han (ORCID: https://orcid.org/0000-0002-6289-4527)
- Zheng Zhang (ORCID: https://orcid.org/0000-0003-2350-3949)
- Shuaibo He
- Wenqiang Li (ORCID: https://orcid.org/0009-0002-2593-0194)
- Rumei Dong
Institutions
- Qinghai Provincial Peoples Hospital (CN)
- Gansu Great Wall Electrical and Electronics Engineering Research Institute (CN)
- First Hospital of Lanzhou University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Reviews in Cardiovascular Medicine
- Published
- 2025-09-25
- DOI
- https://doi.org/10.31083/rcm39271
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China