Construction of a Risk Prediction Model for Postpartum Stress Urinary Incontinence Based on Machine Learning
Pregnancy pregnancy and childbirth is one of the main causes of Stress Urinary Incontinence (SUI). SUI not only affects women's physical health, but also affects women's mental health. 48 puerperae with SUI 6-8 weeks postpartum and 118 puerperae without urinary incontinence during the same period were selected in a hospital in eastern China. Patient information was retrieved from medical records, and postpartum women were asked to complete the International Urinary Incontinence Counseling Questionnaire Short Form (ICI-Q-SF). The early prediction model of SUI was constructed based on the random forest ensemble learning method. Compared with the results of the traditional logistic regression model, the random forest model has better prediction performance and can be used as a screening tool for high-risk groups of SUI during pregnancy to guide clinical work.
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