  Background: Postpartum urinary incontinence (PUI) is a common issue among
postnatal women. Previous studies identified potential related variables, but
lacked analysis on certain intrinsic and extrinsic patient variables during
pregnancy.
  Objective: The study aims to evaluate the most influential variables in PUI
using machine learning, focusing on intrinsic, extrinsic, and combined variable
groups.
  Methods: Data from 93 pregnant women were analyzed using machine learning and
oversampling techniques. Four key variables were predicted: occurrence,
frequency, intensity of urinary incontinence, and stress urinary incontinence.
  Results: Models using extrinsic variables were most accurate, with 70%
accuracy for urinary incontinence, 77% for frequency, 71% for intensity, and
93% for stress urinary incontinence.
  Conclusions: The study highlights extrinsic variables as significant
predictors of PUI issues. This suggests that PUI prevention might be achievable
through healthy habits during pregnancy, although further research is needed
for confirmation.
