Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data
The present study constructed multiple models using 50 features from the blood routine and biochemical detection data for the diagnosis of various circulation system diseases. At the same time, the unique hematologic features of various circulation system diseases, including some metabolic-related indicators, were also explored. This cost-effective work will benefit more people and help diagnose and prevent circulation system diseases.
Authors
- Wanshan Ning (ORCID: https://orcid.org/0000-0002-1410-7891)
- Qun Chen (ORCID: https://orcid.org/0000-0002-0851-436X)
- Yunyun Yang (ORCID: https://orcid.org/0000-0002-6972-0896)
- Shuai Liu (ORCID: https://orcid.org/0000-0003-2867-4683)
- Ying Gu (ORCID: https://orcid.org/0000-0001-6501-302X)
- Guolin Hong (ORCID: https://orcid.org/0000-0003-0641-9438)
- Lindan Huang
- Zhicheng Wang (ORCID: https://orcid.org/0000-0001-9151-942X)
Institutions
- Xiamen University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Xiamen University of Technology (CN)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2024-09-28
- DOI
- https://doi.org/10.1186/s12933-024-02439-0
- Citations
- 59
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 41.76
Funders
- National Natural Science Foundation of China