Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Machine learning algorithm predicts urethral stricture following transurethral prostate resection
World Journal of Urology · Aralık 2024
YÖKSİS Kayıtları
Machine learning algorithm predicts urethral stricture following transurethral prostate resection
World Journal of Urology · 2024 SCI-Expanded
PROFESÖR MEHMET KAYNAR →
Machine learning algorithm predicts urethral stricture following transurethral prostate resection
World Journal of Urology · 2024 SCI-Expanded
PROFESÖR SERDAR GÖKTAŞ →
Machine learning algorithm predicts urethral stricture following transurethral prostate resection
World Journal of Urology · 2024 SCI-Expanded
DOÇENT ALİ FURKAN BATUR →
Machine learning algorithm predicts urethral stricture following transurethral prostate resection
World Journal of Urology · 2024 SCI-Expanded
DOÇENT MURAT GÜL →
Machine learning algorithm predicts urethral stricture following transurethral prostate resection
World Journal of Urology · 2024 SCI-Expanded
DOÇENT EMRE ALTINTAŞ →
Makale Bilgileri
DergiWorld Journal of Urology
Yayın TarihiAralık 2024
Cilt / Sayfa42
Scopus ID2-s2.0-85193206997
Erişim🔓 Açık Erişim
Özet
Purpose: To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters. Methods: A retrospective analysis of data from patients who underwent bipolar-TURP encompassing patient characteristics, preoperative routine blood test outcomes, and post-surgery uroflowmetry were used to develop and educate machine learning models. Various metrics, such as F1 score, model accuracy, negative predictive value, positive predictive value, sensitivity, specificity, Youden Index, ROC AUC value, and confidence interval for each model, were used to assess the predictive performance of machine learning models for urethral stricture development. Results: A total of 109 patients’ data (55 patients without urethral stricture and 54 patients with urethral stricture) were included in the study after implementing strict inclusion and exclusion criteria. The preoperative Platelet Distribution Width, Mean Platelet Volume, Plateletcrit, Activated Partial Thromboplastin Time, and Prothrombin Time values were statistically meaningful between the two cohorts. After applying the data to the machine learning systems, the accuracy prediction scores for the diverse algorithms were as follows: decision trees (0.82), logistic regression (0.82), random forests (0.91), support vector machines (0.86), K-nearest neighbors (0.82), and naïve Bayes (0.77). Conclusion: Our machine learning models’ accuracy in predicting the post-TURP urethral stricture probability has demonstrated significant success. Exploring prospective studies that integrate supplementary variables has the potential to enhance the precision and accuracy of machine learning models, consequently progressing their ability to predict post-TURP urethral stricture risk.
Yazarlar (8)
1
Emre Altıntaş
ORCID: 0000-0002-6797-3937
2
Ali Şahin
ORCID: 0000-0002-3139-8205
3
Huseyn Babayev
ORCID: 0000-0001-6985-9436
4
Murat Gul
ORCID: 0000-0002-6657-6227
5
Ali Furkan Batur
ORCID: 0000-0001-7945-7326
6
Mehmet Kaynar
ORCID: 0000-0002-6957-9060
7
Ozcan Kilic
8
Serdar Goktas
ORCID: 0000-0001-6538-7187
Anahtar Kelimeler
Machine learning,blood parameters
Transurethral prostate resection
Urethral stricture
Kurumlar
Selçuk Tip Fakültesi
Konya Turkey
Universität Zürich, Medizinische Fakultät
Zurich Switzerland
Metrikler
3
Atıf
8
Yazar
3
Anahtar Kelime