An interpretable LightGBM model for predicting coronary heart disease: Enhancing clinical decision-making with machine learning
SHAP analysis revealed that age, smoking status, diabetes, hypertension, and high cholesterol were the most influential features in predicting CHD risk. The developed CHD scoring system provided a user-friendly tool for clinicians to assess patient risk levels effectively.
Authors
- Kongjie Lu
- Lang Deng (ORCID: https://orcid.org/0000-0003-3558-7836)
- Huanhuan Hu (ORCID: https://orcid.org/0000-0002-3259-1488)
Institutions
- Zhejiang Chinese Medical University (CN)
- Huzhou Normal University (CN)
- Huzhou Central Hospital (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-09-12
- DOI
- https://doi.org/10.1371/journal.pone.0330377
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 14.69