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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| Format: | Article |
| Language: | English |
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Korean Continence Society
2024-11-01
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| Series: | International Neurourology Journal |
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| Online Access: | http://einj.org/upload/pdf/inj-2448360-180.pdf |
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| author | Hyungkyung Shin Kwang Jin Ko Wei-Jin Park Deok Hyun Han Ikjun Yeom Kyu-Sung Lee |
| author_facet | Hyungkyung Shin Kwang Jin Ko Wei-Jin Park Deok Hyun Han Ikjun Yeom Kyu-Sung Lee |
| author_sort | Hyungkyung Shin |
| collection | DOAJ |
| description | 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 underactivity (DUA) using a comprehensive dataset that includes patient-reported outcomes, uroflowmetry measurements, and ultrasound-derived features. Methods The dataset used in this study was collected from male patients aged 40 and older who presented with LUTS and sought treatment at the urology department of Samsung Medical Center. We developed and trained CatBoost and XGBoost models using this dataset. These models incorporated features like prostate size, voiding parameters, and responses from questionnaires. Their performance was assessed using standard metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Results The results indicated that the CatBoost models displayed greater sensitivity, rendering them effective for initial screenings by accurately identifying true positive cases. Conversely, the XGBoost models showed higher specificity and precision, making them more suitable for confirming diagnoses and reducing false positives. In terms of overall performance for both BOO and DUA, XGBoost surpassed CatBoost, achieving an AUROC of 0.826 and 0.819, respectively. Conclusions 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. |
| format | Article |
| id | doaj-art-dca2fc667caa40e9b081d846a5cf9301 |
| institution | Kabale University |
| issn | 2093-4777 2093-6931 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Korean Continence Society |
| record_format | Article |
| series | International Neurourology Journal |
| spelling | doaj-art-dca2fc667caa40e9b081d846a5cf93012024-11-29T00:48:20ZengKorean Continence SocietyInternational Neurourology Journal2093-47772093-69312024-11-0128Suppl 2S748110.5213/inj.2448360.1801131Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract SymptomsHyungkyung Shin0Kwang Jin Ko1Wei-Jin Park2Deok Hyun Han3Ikjun Yeom4Kyu-Sung Lee5 Acryl Advanced AI Research Center, Acryl Inc., Seoul, Korea Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea Acryl Advanced AI Research Center, Acryl Inc., Seoul, Korea Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea Acryl Advanced AI Research Center, Acryl Inc., Seoul, Korea Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, KoreaPurpose 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 underactivity (DUA) using a comprehensive dataset that includes patient-reported outcomes, uroflowmetry measurements, and ultrasound-derived features. Methods The dataset used in this study was collected from male patients aged 40 and older who presented with LUTS and sought treatment at the urology department of Samsung Medical Center. We developed and trained CatBoost and XGBoost models using this dataset. These models incorporated features like prostate size, voiding parameters, and responses from questionnaires. Their performance was assessed using standard metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Results The results indicated that the CatBoost models displayed greater sensitivity, rendering them effective for initial screenings by accurately identifying true positive cases. Conversely, the XGBoost models showed higher specificity and precision, making them more suitable for confirming diagnoses and reducing false positives. In terms of overall performance for both BOO and DUA, XGBoost surpassed CatBoost, achieving an AUROC of 0.826 and 0.819, respectively. Conclusions 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.http://einj.org/upload/pdf/inj-2448360-180.pdfartificial intelligencebladder outlet obstructiondiagnosislower urinary tract symptomsurinary bladder, underactive |
| spellingShingle | Hyungkyung Shin Kwang Jin Ko Wei-Jin Park Deok Hyun Han Ikjun Yeom Kyu-Sung Lee Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms International Neurourology Journal artificial intelligence bladder outlet obstruction diagnosis lower urinary tract symptoms urinary bladder, underactive |
| title | Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms |
| title_full | Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms |
| title_fullStr | Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms |
| title_full_unstemmed | Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms |
| title_short | Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms |
| title_sort | machine learning models for the noninvasive diagnosis of bladder outlet obstruction and detrusor underactivity in men with lower urinary tract symptoms |
| topic | artificial intelligence bladder outlet obstruction diagnosis lower urinary tract symptoms urinary bladder, underactive |
| url | http://einj.org/upload/pdf/inj-2448360-180.pdf |
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