Interpretable machine learning for predicting isolated basal septal hypertrophy
Machine learning can effectively predict BSH, with IVS-AO Angle identified as an independent predictor. The RF model, being simple to operate, can be applied to the risk management of BSH patients.
Authors
- Xingyu He (ORCID: https://orcid.org/0000-0003-1125-0411)
- Qiqi Jia (ORCID: https://orcid.org/0000-0003-4946-1353)
- Lei Gao (ORCID: https://orcid.org/0000-0002-9328-6014)
- Boyan Tian
- Yueheng Wang (ORCID: https://orcid.org/0000-0003-2225-3853)
- Guannan Zhao (ORCID: https://orcid.org/0009-0006-0867-2962)
Institutions
- Hebei Medical University (CN)
- Second Hospital of Hebei Medical University (CN)
- Baoding People's Hospital (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-06-30
- DOI
- https://doi.org/10.1371/journal.pone.0325992
- Citations
- 1
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 1.39