Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models
The prediction models developed based on three machine learning algorithms can accurately predict the stone-free rate after PCNL in patients with urinary tract stones. Among these, the GBDT model can effectively identify patients who are most likely to achieve successful outcomes from PCNL based on demographic and stone characteristics, thereby assisting in clinical treatment decision-making.
Authors
- Jie Zhou (ORCID: https://orcid.org/0000-0002-2948-3873)
- Zhengtao Yu (ORCID: https://orcid.org/0009-0009-4753-3529)
- Zhao Rong Liu
- Jian Huang (ORCID: https://orcid.org/0000-0002-5992-2777)
Institutions
- Nanchang University (CN)
- Jiangxi Provincial Cancer Hospital (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- Yuxian People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-19
- DOI
- https://doi.org/10.3389/fmed.2025.1559613
- Citations
- 3
- Primary Topic
- Kidney Stones and Urolithiasis Treatments
- Type
- article
- Field-Weighted Citation Impact
- 4.56