Machine learning-based screening of heart failure using the integrated features of electrocardiogram and phonocardiogram: a multicenter study in China
The ML model incorporating general data alongside ECG and PCG features carried out good detection performance for HF. This had the potential to be an available tool for clinicians to screen HF patients as early as possible for further clinical interventions.
Authors
- Kok Han Chee (ORCID: https://orcid.org/0000-0002-4485-0504)
- Peili Chen (ORCID: https://orcid.org/0000-0002-4766-4053)
- Hua-Nong Ting (ORCID: https://orcid.org/0000-0001-6295-1651)
- Shixi Zhang (ORCID: https://orcid.org/0000-0001-6649-622X)
- Chengyu Liu (ORCID: https://orcid.org/0000-0003-1965-3020)
- Hongwei Sun (ORCID: https://orcid.org/0000-0003-2440-2891)
- Jianwei Bian
Institutions
- University of Malaya (MY)
- Shangqiu Institute of Technology (CN)
- Shangqiu First People's Hospital (CN)
- Tibetan Traditional Medical College (CN)
- Hefei First People's Hospital (CN)
- Hefei Infectious Disease Hospital (CN)
- Jining Medical University (CN)
- Southeast University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-11-21
- DOI
- https://doi.org/10.3389/fcvm.2025.1613577
- Primary Topic
- Phonocardiography and Auscultation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00