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Postoperative complication severity prediction in penile prosthesis implantation: a machine learning-based predictive

Ali Ünal¹, Ali Şahin², Mesut Altan³

¹Department of Urology, Oltu State Hospital, Erzurum, Turkey.

International Journal of Impotence Research

|July 15, 2025

Summary

Machine learning models can predict penile prosthesis implantation complications. Gradient Boosting (GB) showed the best overall performance, improving surgical decision-making and patient outcomes.

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Area of Science:

Urology
Medical Informatics
Machine Learning in Healthcare

Background:

Postoperative complication prediction is vital for penile prosthesis implantation.
Accurate prediction aids in surgical planning and patient management.

Purpose of the Study:

To evaluate machine learning algorithms for predicting postoperative complications after penile prosthesis implantation.
To identify the most effective algorithm for complication prediction.

Main Methods:

Retrospective analysis of demographic, clinical, laboratory, and surgical data (2015-2023).
Trained six machine learning models: Gradient Boosting (GB), AdaBoost, Support Vector Machine (SVM), Random Forest (RF), XGBoost (XGB), and Naive Bayes (NB).
Assessed model performance using accuracy, F1 score, sensitivity, specificity, Youden Index, and AUC.

Main Results:

Gradient Boosting (GB) achieved the highest F1 score (0.86) and superior sensitivity.
Random Forest (RF) demonstrated the highest specificity (1.00).
GB excelled in predicting mild complications (0.74), while Naive Bayes (NB) was best for severe complications (0.94). Key predictors included HbA1c, total testosterone, and urea.

Conclusions:

Gradient Boosting (GB) is a highly effective machine learning model for predicting postoperative complications in penile prosthesis implantation.
Implementing GB-based models can enhance surgical decision-making and potentially improve patient outcomes.
Further research can explore integrating these predictive models into clinical workflows.