Evaluation of the Risk of Urinary System Stone Recurrence Using Anthropometric Measurements and Lifestyle Behaviors in a Developed Artificial Intelligence Model
Yazarlar (11)
Öğr. Gör. Hikmet Yaşar Sancaktepe Training And Research Hospital, Türkiye
Dr. Öğr. Üyesi Kadir Yıldırım Elazig Fethi Sekin City Hospital, Türkiye
Dr. Öğr. Üyesi Mücahit Karaduman Malatya Turgut Ozal University, Türkiye
Bayram Kolcu
Elazig Fethi Sekin City Hospital, Türkiye
Elazig Fethi Sekin City Hospital, Türkiye
Doç. Dr. Mehmet EZER Kafkas Üniversitesi, Türkiye
Ferhat Yakup Suçeken
Umraniye Training And Research Hospital, Türkiye
Umraniye Training And Research Hospital, Türkiye
Fatih Bıçaklıoğlu
Kartal Dr. Lutfi Kirdar Training And Research Hospital, Türkiye
Kartal Dr. Lutfi Kirdar Training And Research Hospital, Türkiye
Mehmet Erhan Aydın
City Training And Educational Hospital, Türkiye
City Training And Educational Hospital, Türkiye
Arş. Gör. Coşkun Kaya City Training And Educational Hospital, Türkiye
Doç. Dr. Muhammed Yıldırım Malatya Turgut Ozal University, Türkiye
Doç. Dr. Kemal Sarıca Sancaktepe Training And Research Hospital, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Diagnostics (Q1) | ||
| Dergi ISSN | 2075-4418 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 10-2025 |
| Cilt / Sayı / Sayfa | 15 / 20 / 2643–0 | DOI | 10.3390/diagnostics15202643 |
| Makale Linki | https://doi.org/10.3390/diagnostics15202643 | ||
| UAK Araştırma Alanları |
Üroloji
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| Özet |
| Background/Objectives Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs. |
| Anahtar Kelimeler |
| artificial intelligence | autoencoder | clinical decision support system | stone recurrence | urinary system stone disease |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 1 |