Development of Machine Learning Prediction Models for
Postoperative Outcomes in Adult Male Circumcision

Leonid Shpaner, M.S., Giuseppe Saitta, M.D.

BMC Urology Paper (HTML) CircumScore App Slides Model Dashboard GitHub Repository

CircumScore App

Patient Outcome Profiler

Slideshow Presentation

From Conceptualization to Modeling

Circumcision Techniques in Milan

A Comparative Modeling Dashboard

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Scatter Plots of Continuous Predictor Variables

Relationships of All Possible Non-Binary Independent Variable Relationships

Exploratory Data Analysis Notebook


Code Notebook: Modeling and Evaluation


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3D Support Vector Machine Decision Boundary

Intraoperative Blood Loss (ml) vs. Surgical Technique

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Data Table Supplement

Supplementary Data Tables

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Supplementary Slides

Exploratory Data Analysis (A-Z)

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BMC Urology Paper

PDF version

References

  1. Mehta KS, Marfatia YS, Jain AP, Shah DJ, Baxi DS. Male circumcision and sexually transmitted infections – an update. Indian J Sex Transm Dis AIDS. 2021;42(1):1–6. https://doi.org/10.4103/ijstd.ijstd_20_21
  2. Ronchi P, Manno S, Dell'Atti L, et al. Technology meets tradition: CO2 laser circumcision versus conventional surgical technique. Res Rep Urol. 2020;12:309–316.
  3. Leonardi R, Saitta G. Laser circumcision in adult males: a modern approach for improved outcomes. In: Zaghal A, El Safadi A, editors. Circumcision – Advances and New Perspectives. London: IntechOpen; 2022. https://doi.org/10.5772/intechopen.106084
  4. Funnell A, Shpaner L, Petousis P. Model Tuner [Software]. Version 0.0.34b1. Zenodo; 2024. https://doi.org/10.5281/zenodo.12727322
  5. Shpaner L, Gil O. EDA Toolkit [Software]. Version 0.0.19. Zenodo; 2024. https://doi.org/10.5281/zenodo.13162633
  6. Shpaner L. Model Metrics [Software]. Version 0.0.4a10. Zenodo; 2024. https://doi.org/10.5281/zenodo.14879819
  7. Harris CR, Millman KJ, van der Walt SJ, et al. Array programming with NumPy. Nature. 2020;585(7825):357–362. https://doi.org/10.1038/s41586-020-2649-2
  8. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–2830. https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html
  9. Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765–4774. PDF
  10. Chou AC, Laih CY, Ku FY. A retrospective Taiwanese-population-based clinical study on determining the efficacy and safety of disposable circumcision anastomat. J Clin Med. 2022;11(20):6206. https://doi.org/10.3390/jcm11206206
  11. Demas CP, Khan S, Mandava SH, et al. The effect of diabetes on postoperative outcomes following male urethral sling placement. Can Urol Assoc J. 2016;10(7–8):E251–E254. https://doi.org/10.5489/cuaj.3613
  12. Talini C, Antunes LA, de Carvalho BCN, et al. Circumcision: postoperative complications that required reoperation. Einstein (São Paulo). 2018;16(3):eAO4241. https://doi.org/10.1590/S1679-45082018AO4241
  13. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17:230. https://doi.org/10.1186/s12916-019-1466-7