Related Experiment Video
Updated: Jun 6, 2025
Author Spotlight: Establishing a Rodent Model for Investigating Depression Factors in Traditional Mongolian Medicine
Published on: October 27, 2023
Machine-Learning-Based Predictive Model for Bothersome Stress Urinary Incontinence Among Parous Women in Southeastern
Qi Wang1,2,3, Xiaoxiang Jiang1,2,3, Xiaoyan Li1,2,3
1Department of Gynecology, Fujian Maternity and Child Health Hospital, 18 Dao-Shan Street, Gu-Lou District, Fuzhou, 350000, PR China.
Introduction And Hypothesis:
Accurate identification of female populations at high risk for urinary incontinence (UI) and early intervention are potentially effective initiatives to reduce the prevalence of UI. We aimed to apply machine-learning techniques to establish, internally validate, and provide interpretable risk assessment tools.
Methods:
Data from a cross-sectional epidemiological survey of female urinary incontinence conducted in 2022 were used. Sociodemographic and obstetrics-related characteristics, comorbidities, and urinary incontinence questionnaire results were used to develop multiple prediction models. Seventy percent of the individuals in the study cohort were employed in model training, and the remainder were used for internal validation. Model performance was characterized by area under the receiver-operating characteristic curve (AUC) and calibration curves, as well as Brier scores. The best-performing model was finally selected to develop an online prediction tool.
Results:
The results showed that bothersome stress urinary incontinence (BSUI) occurred in 9.6% (849 out of 8,830) of parous women. The XGBoost model achieved the best prediction performance (training set: AUC 0.796, 95% confidence interval [CI]: 0.778-0.815, validation set: AUC 0.720, 95% CI: 0.686-0.754). Additionally, the XGBoost model achieved the lowest (best) Brier score among the models, with sensitivity of 0.657, specificity of 0.690, accuracy of 0.688, positive predictive value of 0.231, and negative predictive value of 0.948. Based on this model, the top five risk factors for the development of BSUI among parous women were ranked as follows: body mass index, age, vaginal delivery, constipation, and maximum fetal birth weight. An online calculator was provided for clinical use.
Conclusion:
The application of machine-learning algorithms provides an acceptable, though not perfect, prediction of BSUI risk among parous women, requiring further validation and improvement in future research.
