Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms
Purpose This study aimed to develop and evaluate machine learning models, specifically CatBoost and extreme gradient boosting (XGBoost), for diagnosing lower urinary tract symptoms (LUTS) in male patients. The objective is to differentiate between bladder outlet obstruction (BOO) and detrusor undera...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Korean Continence Society
2024-11-01
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| Series: | International Neurourology Journal |
| Subjects: | |
| Online Access: | http://einj.org/upload/pdf/inj-2448360-180.pdf |
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