Developing an Interpretable Machine Learning Model for Early Prediction of Cardiovascular Involvement in Systemic Lupus Erythematosus
This study is the first to develop an interpretable ML model to predict the risk of cardiac involvement in SLE. Notably, the GBM model showed optimal performance, and its interpretability allowed clinicians to visualize decision-making processes, facilitating early identification of high-risk patients.
Authors
- Qiyun Liu (ORCID: https://orcid.org/0009-0009-9545-7962)
- Feng Chen (ORCID: https://orcid.org/0000-0001-5671-6962)
- Xiaoyu Wang (ORCID: https://orcid.org/0000-0003-1560-0255)
- Chuangye Lyu
- Zixian Deng
- Jianghua Li (ORCID: https://orcid.org/0000-0002-5753-7744)
- Tangzhiming Li (ORCID: https://orcid.org/0000-0002-5273-9531)
- Huadong Liu (ORCID: https://orcid.org/0000-0003-2042-2457)
- Caiping Wang (ORCID: https://orcid.org/0009-0007-2652-3407)
Institutions
- Jinan University (CN)
- Peking University (CN)
- Southern University of Science and Technology (CN)
- Longgang Central Hospital (CN)
- Hegang People's Hospital (CN)
- Peking University First Hospital (CN)
- Shenzhen Second People's Hospital (CN)
Publication Details
- Journal
- Journal of Inflammation Research
- Published
- 2025-07-01
- DOI
- https://doi.org/10.2147/jir.s526608
- Citations
- 4
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 5.55
Funders
- National Natural Science Foundation of China
- Guangdong Medical Research Foundation
- Sanming Project of Medicine in Shenzhen
- Science and Technology Planning Project of Shenzhen Municipality
- Basic and Applied Basic Research Foundation of Guangdong Province