[Risk factors of urinary incontinence in Chinese women based on random forest]

Objective: To explore the risk factors of urinary incontinence (UI) in China by using random forest algorithm, and to evaluate the predictive effect of each risk factor on UI. Methods: A baseline survey with a multistage stratified cluster sampling design was conducted between February 2014 and January 2016, and followed up by telephone from June to December 2018. A total of 55 477 adult women from six provinces of China participated the survey. According to the ratio of 1:1, under sampling method was used to randomly select the same number of women as UI from the non UI women. The data were randomly divided into training set and verification set according to 7:3. The training set was used to establish the random forest model, which including the candidate variables with P<0.2 in univariate analysis, and the verification set was used to verify the predictive effects. Results: A total of 30 658 patients (55.26%, 30 658/55 477) completed the follow-up, the median follow-up time was 3.7 years. Among the 24 985 women without UI at baseline, 1 757 (7.03%, 1 757/24 985) had UI at followed up, including 1 117 (4.47%, 1 117/24 985) with stress UI, 243 (0.97%, 243/24 985) with urgency UI and 397 (1.59%, 397/24 985) with mixed UI. When fixed the number of features as 2 and the number of random trees as 300 in the random forest model, the out of bag error rate estimation was the lowest; with such parameter settings, the classification accuracy was 64.3%, the sensitivity was 64.2%, and the specificity was 64.4%. The top10 predictive UI factors that screening by the variable importance measure in random forest model were obtained as follows: age, parity, delivery pattern, body mass index (BMI), menopause, history of diabetes, education level, history of pelvic surgery, regions, and marital status. Conclusion: We identified the top10 predictive UI factors that screening by the variable importance in random forest model as follows: age, parity, delivery pattern, BMI, menopause, history of diabetes, education level, history of pelvic surgery, regions, and marital status.