Machine Learning Predictions of Postoperative
Outcomes in Adult Male Circumcision

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

CircumScore App Slideshow 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

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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)

References

  1. Leonardi R, Saitta G. Laser Circumcision in Adult Males: A Modern Approach for Improved Outcomes. In: Surgical Advances in Urology. IntechOpen; 2022. https://doi.org/10.5772/intechopen.106084
  2. Lundberg SM, Lee S-I. (2017). A unified approach to interpreting model predictions. NeurIPS, 30: 4765–4774. PDF
  3. 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
  4. 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
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