Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms
Integrating these machine learning models into the diagnostic workflow for LUTS can significantly enhance clinical decision-making by offering noninvasive, cost-effective, and patient-friendly diagnostic alternatives. The combined application of CatBoost and XGBoost models has the potential to improve diagnostic accuracy and provide customized treatment plans for patients, ultimately leading to better clinical outcomes.
Authors
- Kwang Jin Ko (ORCID: https://orcid.org/0000-0003-1085-1266)
- Deok Hyun Han (ORCID: https://orcid.org/0000-0002-9962-802X)
- W.H. Park
- Kyu‐Sung Lee (ORCID: https://orcid.org/0000-0003-0891-2488)
- Ikjun Yeom (ORCID: https://orcid.org/0000-0001-7883-2905)
- H.S. Shin
Institutions
- Korea Institute for Advanced Study (KR)
- Samsung Medical Center (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- International Neurourology Journal
- Published
- 2024-11-29
- DOI
- https://doi.org/10.5213/inj.2448360.180
- Citations
- 8
- Primary Topic
- Urinary Bladder and Prostate Research
- Type
- article
- Field-Weighted Citation Impact
- 4.28
Funders
- National IT Industry Promotion Agency
- Ministry of Science and ICT, South Korea
- Samsung