Neural network model outperforms conventional equations in LDL cholesterol estimation: A comparative study of 188,887 Chinese individuals with focus on hypertriglyceridemia
By overcoming the linear assumptions of conventional equations, the neural network-based model significantly improves LDL-C estimation in hypertriglyceridemia (especially≥9.03 mmol/L) and complex lipid profiles, thereby expanding the applicability of traditional formulas, while demonstrating robust performance across multiple analytical systems.
Authors
- Yu‐Hsien Lin (ORCID: https://orcid.org/0000-0002-2719-0532)
- Ling Ji (ORCID: https://orcid.org/0000-0003-4088-2514)
- Yong Xia (ORCID: https://orcid.org/0000-0002-8611-6455)
- Pengzhen Chen
- Hao Xue (ORCID: https://orcid.org/0000-0001-5915-4811)
- Ye Liu (ORCID: https://orcid.org/0000-0002-8473-6538)
- Mingyang Li (ORCID: https://orcid.org/0000-0001-8927-6406)
- Lin Li (ORCID: https://orcid.org/0000-0002-1626-2713)
Institutions
- Peking University Shenzhen Hospital (CN)
- Mindray (China) (CN)
Publication Details
- Journal
- Atherosclerosis Plus
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1016/j.athplu.2025.11.003
- Primary Topic
- Diabetes, Cardiovascular Risks, and Lipoproteins
- Type
- article
- Field-Weighted Citation Impact
- 0.00