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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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Principais autores: Hyungkyung Shin, Kwang Jin Ko, Wei-Jin Park, Deok Hyun Han, Ikjun Yeom, Kyu-Sung Lee
Format: Artigo
Sprog:Inglês
Udgivet: Korean Continence Society 2024-11-01
Serier:International Neurourology Journal
Fag:
artificial intelligence
bladder outlet obstruction
diagnosis
lower urinary tract symptoms
urinary bladder, underactive
Online adgang:http://einj.org/upload/pdf/inj-2448360-180.pdf
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