Development of a machine learning model for hepatic steatosis screening using non-invasive Traditional Chinese Medicine diagnostics and clinical variables: a health checkup study with community screening potential
TCM features combined with anthropometric variables can be used to develop a non-invasive screening model for ultrasound-diagnosed hepatic steatosis. Both the XGBoost and Logistic Regression models demonstrated robust performance, though external validation is needed to confirm generalizability. This non-invasive approach offers a practical tool with potential for hepatic steatosis screening in community settings.
Authors
- L F Li
- Ke Zhu
- Bing Lin
- Zhihui Zhao
- Weihong Li
- Sheng Zheng
Institutions
- Third Affiliated Hospital of Southern Medical University (CN)
- Mianyang Institute of Traditional Chinese Medicine (CN)
- Guangdong Provincial Hospital of Traditional Chinese Medicine (CN)
- Gannan Medical University (CN)
- Sichuan Academy of Traditional Chinese Medicine (CN)
- Chengdu University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-14
- DOI
- https://doi.org/10.3389/fmed.2025.1704441
- Primary Topic
- Traditional Chinese Medicine Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chengdu University of Traditional Chinese Medicine
- Chengdu University